Behavior Research Methods, Instruments, & Computers2000,32 (4), 588-615

Naming times and standardized normsfor the Italian PDIDPSS set of 266 pictures:Direct comparisons with American, English,

French, and Spanish published databases

ROBERTO DELIJACQUA, LORELLA LOTTO, and REMO JOBUniversity ofPadua, Padua, Italy

The present study provides Italian nonnative measures for 266 line drawings belonging to the newset of pictures developed by Lotto, Dell'Acqua, and Job (in press). The pictures have been standard­ized on the following measures: number of letters, number of syllables, name frequency, within-categorytypicality, familiarity, age of acquisition, name agreement, and naming time. In addition to providing themeasures, the present study focuses on indirect and direct comparisons (i.e., correlations) of the pres­ent norms with databases provided by comparable studies in Italian (in which nonnative data were col­lected with Snodgrass & Vanderwart's set of pictures; Nisi, Longoni, & Snodgrass, 2000), in British En­glish (Barry, Morrison, & Ellis, 1997), in American English (Snodgrass & Vanderwart, 1980; Snodgrass& Yuditsky, 1996), in French (Alario & Ferrand, 1999), and in Spanish (Sanfeliu & Fernandez, 1996).

It is unquestionable that the standardization of pictor­ial stimuli, such as that carried out in their seminal workby Snodgrass and Vanderwart (1980), had a positive ef­fect on studies of object processing. Such stimuli havebeen used for research purposes in several fields of ex­perimental psychology, with the obvious benefit ofbeingreadily available for selection according to the numberof object characteristics that were obtained followingtheir validation for an American sample. This standard­ized set ofpictorial stimuli has been used in experimentswith adults that focused on the difference in speed be­tween reading words and naming pictures (e.g., Lotto,Rumiati, & Job, 1996; Snodgrass & McCullough, 1986;Vanderwart, 1984) and in perceptual identification andrecognition experiments (e.g., Snodgrass, 1984; Snod­grass & Corwin, 1988; Snodgrass & Poster, 1992). Inpriming experiments, these stimuli have also been mod­ified in order to study the effect of the prior exposure offragmented pictures (e.g., Feenan & Snodgrass, 1990;Snodgrass & Feenan, 1992) or fragmented words (Snod­grass & Poster, 1992) on processing of subsequent stim­uli. Furthermore, in order to adapt the material to popula­tions of different ages, the same set of stimuli has been

This work was supported by a START-UP grant (FFMA, 1998) to theauthors. The authors are grateful to Anna Conti, Massimo Girelli, Ste­fania Marcellino, and Daniele Gasparini for help in collecting the datafor the present study. The authors are indebted to Pierre Jolicceur, JudithKroll, and Gay Snodgrass for useful comments on an earlier version ofthis paper and to Eraldo Nicotra and Massimiliano Pastore for statisti­cal advice. Correspondence concerning this article should be addressedto R. Dell'Acqua, Dipartimento di Psicologia dello Sviluppo e della So­cializzazione, 8, Via Venezia, 35131 Padova, Italy (e-mail: dellacqu@psy.unipd.it).

standardized for 5- to 6-year-old American children (Ber­man, Friedman, Hamberger, & Snodgrass, 1989), and for8- to 10-year-old American children (Cycowicz, Fried­man, Rothstein, & Snodgrass, 1997).

The widespread need for standardized pictorial mate­rial has recently motivated a series of studies in whichnormative data have been collected in different linguis­tic contexts, using Snodgrass and Vanderwart's (1980)set ofpictures. This has been the case for British English(Barry, Morrison, & Ellis, 1997), French (Alario & Fer­rand, 1999), Spanish (Sanfeliu & Fernandez, 1996), Dutch(Marte in, 1995), and Italian (Nisi, Longoni, & Snodgrass,2000). More or less generally across these studies, therated object dimensions concerned the familiarity of theconcepts represented by the pictures, the codability ofthe stimuli, such as the agreement on the names or on theimages elicited by the pictures, the complexity of the vi­sual structure of the pictures, and the age at which theconcepts represented by the pictures were first processedand/or coded into memory. In two previous studies, mea­sures of the time it took to name the pictures were also ob­tained (Barry et al., 1997; Snodgrass & Yuditsky, 1996).

The scope of the present work is twofold. First, thiswork provides norms for a set of 266 pictures standard­ized for an Italian sample. We deviate from the com­monly adopted procedure ofusing Snodgrass and Vander­wart's (1980) pictures. Instead, we presented, to a sampleofItalian subjects (N = 178), a new set ofpictures (Lotto,Dell'Acqua, & Job, in press) that were extracted fromsources other than previously published databases (i.e.,adapted from dictionaries and books). These pictures areavailable in PCX bitmapped format (downloadable bothas a zipped archive and as single items; http://olpss.psy.unipol.it/psychdata.htm). Furthermore, in order to invite

Copyright 2000 Psychonomic Society, Inc. 588

researchers operating in different linguistic communitiesto use the present set of pictures, we provide both indi­rect and direct comparisons between the present norma­tive data and those from previous studies that have usedSnodgrass and Vanderwart's set ofpictures for standard­ization purposes. In doing this, we propose a method bywhich to estimate the relative influence of cultural fac­tors (e.g., linguistic factors and/or those modulated bythe different cultural contexts) and visual factors (i.e.,factors modulated by the visual dissimilarity among ourand Snodgrass & Vanderwart's pictures) on the distribu­tion of the values reported across the studies that we ex­amine.

METHOD

SubjectsA total of 178 students at the University of Padova volunteered

to participate in this study. The age of the subjects ranged from 20to 30 years. All the subjects had normal or corrected-to-normal vi­sion. All the subjects in the naming experiment had normal hearing.

MaterialA set of266 black line drawings of real objects was generated for

the present study. Some ofthe line drawings were created anew, andsome were adapted from illustrations in dictionaries and books. Theobjects belonged to 13 distinct semantic categories (i.e., birds,buildings, clothes, flowers, furniture, fruits, housewares, mammals,musical instruments, receptacles, vegetables,vehicles, and weapons).An additional ad hoc category (i.e., mixed) was created by includ­ing objects belonging to different semantic categories for whichonly a few exemplars were available. The number ofobjects in eachcategory varied from I I to 32. A file format version ofeach picturewas obtained by importing picture hard copies on a computer viascanner. When displayed on the monitor of the computer (back­ground luminance, 28 cd/rn-) in the naming experiment, each pic­ture (about 25 cd/m-) could be inscribed in a square with a side ofless than 5°of visual angle, at a viewing distance ofabout 60 ern. Forthe rating procedure, the set of266 picture hard copies was dividedinto two sublists, with the constraint that all the elements of a givensemantic category were grouped in one sublist. This constraint didnot apply to the pictures in the mixed category. For each sublist, abooklet was created in which each picture was reported in the cen­ter of a separate sheet, together with the name of the picture se­mantic category (above) and an n-point scale (see below), with n de­pending on the rated dimension. For the pictures included in themixed category, the semantic categories reported above the pictureswere work tools (for sickle, scissors, hammer, brush, rake, palette,pincers, and drill), personal effects (for umbrella, pipe, and razor),habitations (for teepee), sweets (for candy), excursion tools (forhammock, binoculars, compass, map, and backpack), house objects(for antenna, candle, globe, radio, faucet, and clock), measurementtools (for hourglass and scale), sport articles (for helmet), toys (forskittle), jewels (for earrings), natural objects (for leaf), space ob­jects (for planet), and plants (for cactus, palm tree, and ivy).

Rating ProcedureWithin-eategory typicality. Thirty subjects took part in the pres­

ent rating task, 15 for each type of sublist. Each subject was in­structed to judge, with no pressure as to speed, how typical eachpicture was within the corresponding category by marking a valueon the scale (I = not typical; 7 = highly typical).

Familiarity. Thirty subjects took part in the present rating task,15 for each type of sublist. None participated in the typicality­rating task. Each subject was instructed to judge, with no pressure

STANDARDIZED PICTURES 589

as to speed, how familiar the object depicted in each picture was,based on his/her own personal experience (I = not familiar; 7 =highly familiar).

Age of acquisition. For the present rating task, each picture inthe booklets was replaced with the corresponding name.' Thirtysubjects took part in the present rating task, 15 for each type of sub­list. None participated in the typicality- and familiarity-rating tasks.Each subject was instructed to indicate, with no pressure as to speed,how old he/she was when he/she first encountered or received in­formation about each word, using the 9-point scale reported belowthe picture (I = 2 years or younger, 2 = 3 years, 3 = 4 years, 4 =

5years,5 = 6years,6 = 7/8years,7 = 9/lOyears, 8 = 1l/12years,and 9 = 13 years or older).

Speeded Naming ProcedureTwo different experimental sessions were required for the nam­

ing data collection. A list of 220 objects was presented in the firstsession. A list of 168 objects was presented in the second session.?In each session, 44 subjects took part in the naming task. The ex­periment was carried out in a soundproof room, with the constantpresence of a research assistant for response-scoring purposes. Thepictures were displayed on the monitor ofthe computer (resolution,640 X 480; cathode ray tube), and the vocal responses were recordedthrough a cardiod microphone. The monitor and microphone set­tings were controlled by a 166-MHz CPU and MEL software.

Each session consisted of three phases, each preceded by the pre­sentation of written instructions on the monitor of the computer.The first phase was devoted to adjustment ofmicrophone settings.Each subject was instructed to read, as fast and accurately as pos­sible, a list of 10 words referring to abstract concepts, presented I ata time at the center of the monitor. An interval of 3 sec elapsed be­tween the presentation of 2 successive words. This phase was re­peated if a single failure in detecting the subject's vocal responseoccurred. At each repetition, the sensitivity threshold of the micro­phone was lowered. The second phase was devoted to practice forthe actual naming experiment. On each of eight trials, a fixationpoint was presented in the center ofthe monitor, which disappearedwhen the research assistant pressed a start button (the space bar onthe keyboard of the computer). After pressing the space bar, an in­terval of400 msec elapsed before the presentation of a warning sig­nal (a 1000-Hz pure tone) for 100 msec. At tone offset, an intervalof700 msec elapsed before the presentation ofa picture in the cen­ter of the monitor. The subjects were instructed to name each pic­ture as fast and accurately as possible, trying to avoid the produc­tion of undesired noise (cough, hesitations, etc.). Each pictureremained in view until a vocal response was detected. An intervalof 2 sec elapsed between response detection and the beginning ofthe next trial. The third phase was dedicated to data collection. Be­fore the beginning of the third phase, the instructions stressed theimportance of speed and accuracy during the whole experiment.Some rest during the experiment was allowed upon request of thesubject. The order of picture presentation was fully randomizedacross subjects.

During the experiment, each response was scored by the researchassistant, using the following scoring rule. A response could be cor­rect (i.e., the name produced by the subject corresponded to thename assigned a priori to each picture), alternative (i.e., the nameproduced by the subject did not correspond to the assigned name),or invalid, reflecting microphone triggering by vocalizations thatwere not name productions.

RESULTS

Trimming of the Naming Time DistributionThe data from 2 subjects in each naming session were

discarded because of software malfunctioning or because

590 DELL'ACQUA, LOTTO, AND JOB

the rate of invalid responses exceeded the rate of bothcorrect and alternative responses. Validnaming data werethus collected from 84 subjects. The analyses ofthe nam­ing times (RTs) concentrated on correct responses. RTswere first screened for outliers, using a modification ofthe procedure proposed by Van Selst and Jolicceur(1994).The RTs for each picture were sorted, and the most ex­treme observation was temporarily excluded from con­sideration. The mean and standard deviation of the re­maining values were then computed. Cutoff values wereestablished, using the following equations:

V10w =X- Cn*SD

and

Vhigh =X+ Cn*SD.

The smallest and largestobservations were then checkedagainst the cutoff values, V10w and Vhigh. If one or bothfell outside the bounds, these observations were excludedfrom further consideration and were defined as outliers.This algorithm was then applied anew to the remainingdata. The value of C depended on the sample size, n, sothat the estimated final mean was not influenced by sam­ple size. For samples of 100 or larger, Cis 3.5, and thevalue of C is increased nonlinearly as sample size de­creases, to a maximum of8.0 for a sample size of4. Notethat, with this algorithm, outliers were calculated on thebasis of the RT distribution for a given picture, not thatfor a given subject. This had the important implication ofavoiding the elimination ofRTs to difficult-to-name pic­tures, which obviously tended to fall into the slowest por­tion of the subject's RT distribution (see Snodgrass &Yuditsky, 1996, for a discussion of this problem). Theapplication of the outlier elimination procedure resultedin a total loss of3.8% of the available RT data.

Normative Data DescriptionAn Excel-formatted version of the normative data is

available on line (http://olpss.psy.unipol.it/psychdata.htm). The complete list of the normative data is reportedin Appendix A. The first 2 columns of the list in Appen­dix A present the pictures' names (correct and alterna­tive), in both Italian and English. Each alternative name(in lowercase, preceded by a right-pointing arrow) is as­sociated with the percentage of subjects (in parentheses)who produced the alternative response. Each picture isassociated with the following indexes. In the 3rd column(labeled CAT), a three-letter abbreviation of the seman­tic category of each picture is reported (i.e., BIR, birds;FRU, fruits; VEH, vehicles; VEG, vegetables; MIX, mixed;WEA, weapons; BUI, building; FOR, furniture; INS, musicalinstruments; MAM, mammals; FLO, flowers; CLO, clothes;REC, receptacles; HOU, housewares). Number of lettersand number of syllables of the pictures' names are re­ported in the 4th and 5th columns (labeled LET andSYL), respectively. Frequency values (log-transform ofI + number of occurrences over one million) of theprinted pictures' names are reported in the 6th column

(labeled FRQ). The source of the frequency values wasStella and Job's (in press) database. In the 7th column(labeled S), the number of the session in which the pic­ture RTs have been collected is reported (I, first session;2, second session). Mean familiarity values are reportedin the 8th column (labeled FAM), together with the rela­tive standard deviations in the 9th column (labeled SD).Mean typicality values are reported in the 10th column(labeled TYP), together with the relative standard devi­ations in the II th column (SD). Mean age of acquisitionvalues are reported in the 12th column (labeled AoA),together with the relative standard deviations in the 13thcolumn (SD). Mean naming times are reported in the14th column (labeled RT). Name agreement and conceptagreement values are reported in the 15th (labeled NA)and 16th (labeled CA) columns, respectively. NA andCA values are reported in the form of the percentages ofsubjects who produced the correct name (for NA) or thecorrect name plus synonyms (CA). Synonyms ofthe cor­rect name (i.e., those whose NA value contributed to thecomputation of the CA value) are marked with an aster­isk. When an NA value associated with a correct name isnot the modal value (i.e., the correct name is not the mostfrequently produced name), all of the more frequent al­ternative names are marked with the symbol @. H val­ues (computed by using the equation described by Snod­grass & Yuditsky, 1996) are reported in the 17th column(labeled H). A strict criterion (Snodgrass & Vanderwart,1980) was adopted to compute the H statistic for eachitem, so that all names that were not identical to the cor­rect name were considered in the computation.

Stepwise Multiple RegressionThe matrix of partial correlation among the measures

for the Italian sample is reported in Table 1A, and the re­sults of the multiple regression analysis are reported inTable IB. The overall equation for the multiple regres­sion was significant [R = .71; F(l0,255) = 25.82, p <.0001]. As can be seen from the results of the multipleregression analysis, the measures of CA and TYP andthe H statistic resulted in reliable predictors of the RTdistribution. AoA figured as a significant predictor ofthe picture RTs in a separate regression in which CA val­ues were temporarily excluded from consideration. Thisseparate analysis was motivated by the possible maskingeffect on the potential reliability of AoA in accountingfor the RT distribution, owing to the strong negative cor­relation between AoA and CA values, as is evidenced inthe partial correlation matrix (see Edwards, 1979).

Comparisons With Previous StudiesIndirect comparisons with previous naming time

studies. Although several studies in this field have re­ported standardized norms for different languages (seethe forthcoming section), picture RTs have been col­lected in only two normative studies in which large sam­ples ofpictures (N) 200) were employed. In this section,we focus on an indirect comparison between the multiple

STANDARDIZED PICTURES 591

Table IAMatrix of Partial Correlations Among the Italian Measures

Measures RT LET SYL FRQ FAM TYP AoA NA CA

LET n.s.SYL .164 .852FRQ -.406 -.345 -.313FAM -.249 n.s. n.s. .236TYP -.231 -.121 n.s. .343 .457AoA .472 n.s. .181 -.519 -.502 -.321NA -.634 -.165 -.236 .422 .220 .134 -.492CA -.671 n.s. -.182 .426 .238 n.s, -.540 .933H .538 n.s. .172 -.279 -.148 n.s. .323 -.798 -.714

Note-RT, naming time; LET, number of letters; SYL, number of syllables; FRQ, frequency;FAM, familiarity; TYp, typicality; AoA, age of acquisition; NA, name agreement; CA, conceptagreement; n.s., nonsignificant.

regression results obtained in the present study and theresults reported by Snodgrass and Yuditsky (1996, Exper­iment 1) and Barry et al. (1997). It is perhaps worth not­ing that RTs in these different studies have been trimmedaccording to different criteria for the elimination ofout­liers. Whereas Snodgrass and Yuditsky applied a crite­rion that is formally similar to the criterion adopted inthe present study, Barry et al. used a reciprocal transfor­mation of each picture's mean RT, following the elimi­nation of RTs that were either shorter than 200 msec orlonger than 3,000 msec. Although it is generally acceptedthat 3,000 msec is a reasonable constraint to adopt forthe exclusion of what the authors treated as "major in­stances ofword-finding difficulties," as was concluded bySnodgrass and Yuditsky, this may not be a viable methodby which to prevent the possibility ofsystematically elim­inating (informative) RTs to difficult-to-name pictures.

A summary ofthe multiple regression results from thepresent study and the two other studies mentioned is re­ported in Table 2A. The letter x is reported for those mea­sures that resulted in reliable predictors of the RT distri­bution in each ofthe studies. When a measure did not enterinto the regression equation in a given study, the mea­sure is reported as nonsignificant (n.s.). When a partie-

Table 18Results of the Multiple Regression Analysis

Measure f3 SE f PLET -.127 .088 1.451 .148SYL .116 .087 1.329 .185FRQ -.095 .058 1.650 .100FAM -.002 .056 0.030 .976TYP -.124 .053 2.356 .019AoA .077 .064 1.192 .234

(.138) (.065) (2.118) (.035)*NA .156 .147 1.060 .290CA -.598 .132 4.529 .000H .163 .075 2.183 .030

Note-For each measure, beta weight (f3I, standard error (SE), t statis­tic value (f), and level of probability (p) are reported. LET, number ofletters; SYL, number of syllables; FRQ, frequency; FAM, familiarity;TYP, typicality; AoA, age of acquisition; NA, name agreement; CA,concept agreement. *AoA results after the temporary exclusion ofCA values from the stepwise regression.

ular measure was not considered in a study, the measureis reported as nonavailable (n.a.).

Table 2A highlights both the similarities and the dif­ferences among the studies. The first important differ­ence is represented by the fact that the TYP measureswere collected and treated as an independent factor onlyin our normative study. This difference is all the morestriking in light of the evidence that TYP is a relevantcognitive dimension when concepts must be categorized(Rosch, 1975) and that TYP plays a significant role inmodulating picture RTs (see, e.g., Jolicoeur, Gluck, &Kosslyn, 1984), as well as other behavioral dependentmeasures (e.g., Malt & Smith, 1984). Convergent withthis earlier empirical evidence, the results ofthe stepwisemultiple regression that we performed on our data setsupport the notion that TYP must be considered in stud­ies in which RTs are used as a dependent variable or,more generally, in studies in which pictures are used asexperimental stimuli.

Table 2A also shows that measures ofNA consistentlyresulted in reliable predictors of the RT distribution. It issomewhat surprising that, contrary to all other studies,NA, in the form of the percentage of subjects who pro­duced a given name in response to a particular picture,was not an element of the regression equation in the pre­sent study. In our view, this finding may reflect the factthat, in quite a large proportion of the cases in the presentstudy, alternative names that were produced were "good"lexical substitutes (i.e., synonyms) of the correct names .This had the likely consequence that variations in thepercentage ofNA (which decreases as the number of al­ternatives increases) could not be directly reflected in theRT distribution. Additional information about the role ofstimulus codability, however, is provided by the signifi­cant weight associated with both the H statistic (consid­ered in all the studies) and measures of CA (consideredonly by Snodgrass & Yuditsky, 1996) in accounting forthe RTs collected in the present study. As has been con­vincingly argued by many investigators (e.g., Alario &Ferrand, 1999; Lachman, 1973; Lachman, Shaffer, &Hennrikus, 1974; Snodgrass & Vanderwart, 1980; Snod­grass & Yuditsky, 1996), both Hand CA measures can

592 DEL~ACQUA,LOTTO,ANDJOB

Table2ASummary of the Results of Multiple Regression Analyses Performed in the PresentStudy and in Two Previous Studies (S&Y: Snodgrass & Yuditsky, 1996; BM&E:

Barry, Morrison, & Ellis, 1997) in Which Picture-Naming Times Were Considered

Study LET SYL FRQ FAM TYP AoA NA CA H

PresentS&YBM&E

n.s. n.s. n.s,n.s. n.s. x¥

n.s.* n.a. x

n.s.x¥

n.s.

xn.a.n.a.

xxx

n.s.xx

xx

n.a.

xxx

Note-LET, number of letters; SYL, number of syllables; FRQ, frequency; FAM, familiarity;TYp,typicality; AoA, age of acquisition; NA, name agreement; CA, concept agreement; n.s., non­significant; n.a., nonavailable; x, significant beta weight; x¥, negligible beta weight. *Picturename length measures as number of phonemes.

be taken as better indexes ofstimulus codability with re­spect to the raw percentage of correct denominations.The H statistic is computed by taking into account thenumber of alternatives produced following the presenta­tion of a particular picture. H differs for pictures havingthe same percentage ofNA but different numbers of al­ternative names produced by subjects. Furthermore, CAestimates reflect the degree of semantic appropriatenessofalternative denominations-that is, CA varies as a func­tion of whether alternative names are synonyms of thecorrect names or names referring to semantically distin­guishable concepts. Given two pictures with the same per­centage ofNA and the same number ofalternative names,CA is higher if one picture elicits more synonyms thandoes the other.

Table 2A indicates that a significant role for AoAmeasures was found in all of the three studies. Further­more, AoA turns out to be a better predictor ofRTs thanare both FAM (nonsignificant or negligible in all thestudies) and FRQ (significant in Barry et al.'s, 1997, workonly). It should be noted that, while it might be theoret­ically relevant to ponder the higher reliability of AoA inpredicting picture RTs as compared with FAM (see Mor­rison, Ellis, & Quinlan, 1992, for details on this issue),our impression is that the discussion about the rathervariable weight ofFRQ in the three regressions examinedin the present context may be limited to the choice ofthedifferent FRQ count databases. Specifically, as Snodgrassand Yuditsky (1996; see also Snodgrass & Vanderwart,1980) did in their study, we used an FRQ count databaseof written words (Stella & Job, in press). Barry et al. re­ported results that were referred to a FRQ count databaseof spoken words. The latter was, reasonably, a choicemore compatible with the vocal responses subjects had toproduce in all the studies (i.e., naming) and may accountfor the fact that FRQ had a significant weight only inBarry et al.'s study.

Table 2A shows also that word length measures (num­ber of letters, phonemes, or syllables) did not playa sig­nificant role in determining the RT distribution in any ofthe three studies.

Direct comparisons with previous normative stud­ies. One potential confound in the comparison describedin the foregoing section resides in the fact that the num­ber of independent variables entering into the different

regression equations was necessarily limited. For thisreason, it is important to note that the list of these vari­ables (e.g., FAM, AoA, etc.) constitutes only a subset ofall the variables that might potentially affect object nam­ing. In each normative study listed in Table 2A, the choiceof the independent variables hypothesized to account forthe RT distribution was substantially determined by threefactors-namely, by previous evidence that demonstratedthe effective role of a particular independent variable ininfluencing object naming, by theoretically grounded pre­dictions about the possible role that an independent vari­able might play in object processing, or by the need to se­lect a set of independent variables common to the majorityofstudies, with the aim ofestimating their relative weightin affecting performance on objects across cultures.

Concerning the latter point, with the view of provid­ing a direct comparison between the present norms andnorms standardized for different populations, a potentialconfound may be represented by the difference in thedrawings used for data collection. For the sake ofclarity,suppose that we have two different pictures of the sameconcept-say, fl pitcher-and suppose that each picturehas been associated with a different NA value followinga rating procedure carried out by presenting one pictureto Italian subjects and the other picture to American sub­jects. As a matter offact, it is impossible to assess whetherthis difference is due to a pure cultural difference in thesubjects' population or to a structural difference in the pic­tures submitted to the subjects' judgment (or to an inter­action between these two factors). It might be that, forexample, the picture presented to the Italian subjects wasless detailed than the American picture, and this, in turn,might have been reflected in a higher degree of uncer­tainty about its identity (Is it a pitcher or a mug?). Simi­lar examples can be offered for the other independent vari­abies considered in our normative study, with all theexamples pointing to the same problem-that is, the im­possibility of distinguishing between cultural and struc­tural factors in determining the values assigned to the rateddimensions.

Fortunately in our case, this problem can be solved bytaking advantage of the presence, in the literature, ofItalian norms from a study in which Snodgrass and Van­derwart's (1980) stimuli were used (Nisi et aI., 2000) andfor which data were collected for a subset of measures

Note-FAM, familiarity; AoA, age of acquisition; H, H statistic: FRQ,name frequency; n.a., nonavailable. *p < .05. The table provides in­dications on the results of z tests performed by comparing the correla­tion coefficients between the present measures and the Italian NL&S'smeasures (in bold) to the correlation coefficients between the presentmeasures and, in turn, the American, French, and Spanish measures.

Table2BCorrelation Coefficients Between a Subset of the Present

Measures and the Measures From a Different Italian SampleCollected Using Snodgrass and Vanderwart's Pictures

(Nisi, Longoni, & Snodgrass, 2000), From an American Sample(Snodgrass & Vanderwart, 1980, for Familiarity, Frequency,

and H measures, and Snodgrass & Yuditsky, 1996,for Age of Acquisition Measures), From a French

Sample (Alario & Ferrand, 1999), and From aSpanish Sample (Sanfeliu & Fernandez, 1996)

that were also included in our study (i.e., for FAM, AoA,and NA). Furthermore, a fair number of concepts (N =

105, reported in Appendix B) were common to our studyand that of Snodgrass and Vanderwart. The method wepropose to use to disentangle cultural and structural in­fluences on the measures provided for our new stimulihinges on the following logic. Weassume that the corre­lation coefficient between our measures and the measurescollected by Nisi et al. for the common set of conceptsprovides the benchmark of the influence of structuralfactors on the distribution of the rating values across thestimuli. That is, given that our subjects and Nisi et al.'ssubjects can be thought ofas having been taken from thesame population (Italian university students), our hy­pothesis is that any deviation from the perfect correla­tion between our measures and those ofNisi et al. can bereasonably accounted for by a difference in the stimuliused in the respective normative studies. Our next step isto compare each of these correlation coefficients withthe correlation coefficients between our measures and themeasures collected for American, French, and Spanishand to interpret any significant difference among thesecorrelation coefficients as indicators of the effective in­fluence of cultural differences on the distribution of therating values across the stimuli.'

A list of the correlation coefficients for the measuresfrom the different studies is reported in Table 2B. Severalcomments are in order. The first comment is related tothe influence of structural factors on the distribution ofthe values for the measures collected on the two Italiansamples. Evidence concerning this point can be derivedfrom the observation of the correlation coefficients be­tween the measures collected in our study and those col­lected in Nisi et al.s (2000) study. These correlation co­efficients are reported in bold in Table 2B. As is clear,although FAM and AoA measures are highly correlatedin the two studies, the degree ofcorrelation between NA

Measures FromPresent Study

FAMAoAHFRQ

Italian

.72

.91

.511.00

American

.68

.80*

.28*

.67*

French

.71

.87

.36

.74*

Spanish

.46*n.a..23*.74*

STANDARDIZED PICTURES 593

measures, although substantial, is somewhat lower thanthat for the other measures. Although a systematic inves­tigation of this issue is beyond the scope of the presentwork, we think we have already provided hints for an ex­planation for this relatively low correlation between NAmeasures. The framework we have adopted in the pres­ent section leads us to suspect that the interpretation re­sides in a difference in the material used for data collec­tion, with NA values being a function of the number and/or the quality of the details reported for the pictures usedin the different studies. It is perhaps worth noting that oursample, in general, showed less variability in the numberofalternative names produced in response to the presen­tation ofthe pictures than Nisi et al.s sample did [t(104) =6.91,p < .001]. Furthermore, this result also suggests that,whereas NA is influenced by structural information, aswas suggested by Sanfeliu and Fernandez (1996), FAandAoA values are produced directly by the concept repre­sented by a given picture.

Cultural differences emerge by comparing the magni­tudes ofthe correlations between the two Italian sampleswith those between our measures and those of the for­eign samples. At first blush, the pattern of correlationsamong the values provided by our sample and the "for­eign" values seem to reflect the pattern of correlationsbetween the values from the two Italian samples. As isevident for each sample, correlations are higher for FAMand AoA values and lower for NA values. This pattern ofresults was expected, on the assumption that NA valuesdepend on language more than do the other measures.Similar results have been reported basically by all thestudies in which formally equivalent comparisons havebeen performed (e.g., Alario & Ferrand, 1999; Sanfeliu& Fernandez, 1996). Significant differences betweenthese correlation coefficients are marked with asterisksin Table 2B. Focusing first on the results of the z testsperformed on the correlation coefficients among NAmeasures, the results in Table 2B suggest that culturaldifferences playa significant role only in the comparisonbetween the Italian values (r = .51) and both the Amer­ican English and the Spanish values (r = .28 and .23, re­spectively). The correlation between the Italian and theFrench values do not reflect any cultural difference onthe distribution ofNA values, with the correlation coef­ficient between our values and the French values (r = .36)being statistically comparable with the correlation coef­ficient between the two Italian samples. A cultural dif­ference is also evident in the results for the FAM mea­sure. Whereas the correlation between our Italian valuesand both the American and the French values (r = .68and .71, respectively) is high and comparable with thecorrelation for the Italian-Italian values (r = .72), thesignificant difference of the Italian-Spanish correlationcoefficient (r = .46) suggests that that two populationsdiffer in the degree of FAM with the same concepts. Asto the AoA measures (collected only in the Americanand French studies), the results reported in Table 28 in-

594 DELL' ACQUA, LOTTO, AND JOB

dicate that, contrary to the correlation between the Italianand the French values (r = .87), the correlation betweenthe Italian and the American values (r = .80) is signifi­cantly different from the Italian-Italian correlation co­efficient and suggest that the distribution of AoA valuesacross the same set of stimuli is effectively influencedby cultural factors. Finally, name FRQ measures were as­sumed to be perfectly correlated between the two Italianstudies. This likely caused the z test among all the othercorrelation coefficients to be highly sensitive to any de­viations from r = 1. Table 2B shows that all the com­parisons between the Italian-Italian correlation coeffi­cient for frequency and the Italian-foreign correlationcoefficients, although quite high (r > .67) in all cases,consistently resulted in a significant difference.

CONCLUSIONS

The main goal of the present work was to present Ital­ian normative measures for a new set of 266 pictures thathave been standardized for NA, FRQ, TYP, and RT.Thesepictures can thus be directly used in research with Italian­speaking subjects. It is our opinion that these pictures willbe useful for researchers involved in different fields ofex­perimental psychology, such as attention, memory, per­ception, and language. The regression analyses presentedin this work have documented the significant role that asubset of the rated object dimensions has in determiningsubjects' naming performance on these objects. Concern­ing this point, the present results indicated that a particu­lar dimension, TYp, which has never been taken into con­sideration in previous normative studies, plays adeterminant role in object naming, with pictures of moretypical elements being named faster than pictures of lesstypical elements. An indirect comparison with previousnaming studies has indicated both similarities and differ­ences, across the Italian, American, and English samples,in the type and number of object dimensions that affectpicture RT. For instance, whereas the H statistic and CAseem to be robust predictors of the RT, a substantial fluc­tuation in weight has been evidenced for name FRQ andFAM across the studies considered in the present work.

Furthermore, with the view of(1) making apparent thediscrepancies between the present normative measuresand the measures provided for linguistically differentsamples (collected by using Snodgrass & Vanderwart's,1980, pictures) and (2) encouraging the use of the pre­sent set ofpictures in order to expand research potential­ities, a set of comparisons has been carried out, in orderto show the relative weight of structural and cultural fac­tors in generating such differences. An estimate of theweight of structural factors on the values reported in thepresent work has been provided through the correlationbetween the present measures and a subset of Italianmeasures collected by using Snodgrass and Vanderwart'spictures. This gave us the opportunity, by performing a

series of z tests on the correlation coefficients betweenthe present measures and the measures provided for non­Italian samples, to separate the influence of structuralfrom cultural factors in the rated dimensions.

REFERENCES

ALARIO, F.-x., & FERRAND, L. (1999). A set of 400 pictures standardizedfor French: Norms for name agreement, image agreement, familiar­ity, visual complexity, image variability, and age of acquisition. Be­havior Research Methods, Instruments. & Computers, 31, 531-552.

BARRY, C., MORRISON, C. M., & ELLIS, A. w. (1997). Naming the Snod­grass and Vanderwart pictures: Effects ofage ofacquisition, frequency,and name agreement. Quarterly Journal ofExperimental Psychology,50A, 560-585.

BERMAN, S., FRIEDMAN, D., HAMBERGER, M., & SNODGRASS. J. G.(1989). Developmental picture norms: Relationships between nameagreement, familiarity, and visual complexity for child and adult rat­ings oftwo sets ofline drawings. Behavior Research Methods. Instru­ments. & Computers, 21, 371-382.

CYCOWICZ, Y. M., FRIEDMAN, D., ROTHSTEIN, M., & SNODGRASS. J. G.(1997). Picture naming by young children: Norms for name agree­ment, familiarity, and visual complexity. Journal of ExperimentalChild Psychology, 65, 171-237.

EDWARDS, A. L. (1979). Multiple regression and the analysis of vari­ance and covariance. New York: w. H. Freeman.

ELLIS, A. W., & MORRISON, C. (1998). Real age-of-acquisition effectsin lexical retrieval. Journal ofExperimental Psychology: Learning.Memory. & Cognition, 24, 515-523.

FEENAN, K., & SNODGRASS, J. G. (1990). The effect of context on dis­crimination and bias in recognition memory for pictures and words.Memory & Cognition, 18,515-527.

JOLICCEUR, P., GLUCK, M. A., & KOSSLYN, S. M. (1984). Pictures andnames: Making the connection. Cognitive Psychology, 16,243-275.

LACHMAN, R. (1973). Uncertainty effects on time to access the internallexicon. Journal ofExperimental Psychology, 99, 199-208.

LACHMAN, R., SHAFFER, J. P., & HENNRIKUS, D. (1974). Language andcognition: Effects of stimulus codability, name-word frequency, andage of acquisition on lexical reaction time. Journal ofVerbal Learn­ing & VerbalBehavior, 13,613-625.

LOTTO, L., DELL' ACQUA, R., & JOB, R. (in press). Le figure PDIDPSS:Misure ~i accordo sui nome, tipicita, familiarita, eta di acquisizionee tempi di reazione per 266 figure [The PD/DPSS pictures: Normsfor name agreement, typicality, familiarity, age of acquisition, andnaming times for 266 pictures]. Giornale Italiano di Psicologia.

LOTTO, L., RUMIATI, R., & JOB, R. (1996). The picture superiority ef­fect in categorization: Visual or semantic? Journal ofExperimentalPsychology: Learning, Memory. & Cognition, 18, 1019-1028.

MALT, B. C; & SMITH, E. E. (1984). Correlated properties in naturalcategories. Journal ofVerbalLearning & VerbalBehavior, 23,250-269.

MARTElN, R. (1995). Norms for name and concept agreement, familiar­ity, visual complexity and image agreement on a set of216 pictures.Psychologica Belgica, 35, 205-225.

MORRISON, C. M., ELLIS, A. w., & QUINLAN, P. T. (1992). Age of ac­quisition, not word frequency, affects objects naming, not object rec­ognition. Memory & Cognition, 20, 705-714.

NISI,M., LONGONI, A. M., & SNODGRASS, J. G. (2000). Misure italianeper I'accordo sui nome, familiarita ed eta di acquisizione, per Ie 260figure di Snodgrass e Vanderwart (1980) [Italian norms for nameagreement, familiarity, and age of acquisition for the S&V (1980) setof pictures]. Giornale Italiano di Psicologia, 27, 205-218.

ROSCH, E. (1975). Cognitive representation of semantic categories.Journal ofExperimental Psychology: General, 104, 192-233.

SANFELIU, M. c., & FERNANDEZ, A. (1996). A set of254 Snodgrass­Vanderwart pictures standardized for Spanish: Norms for nameagreement, image agreement, familiarity, and visual complexity. Be­havior Research Methods. Instruments. & Computers, 28, 537-555.

SNODGRASS, J. G. (1984). Concepts and their surface representations.Journal ofVerbal Learning & Verbal Behavior, 23, 3-22.

SNODGRASS, J. G., & CORWIN, J. (1988). Perceptual identification thresh­olds for 150 fragmented pictures from the Snodgrass and Vanderwartpicture set. Perceptual & Motor Skills, 67, 3-36.

SNODGRASS, J. G., & FEENAN, K. (1992). Priming effects in picture frag­ment completion: Support for the perceptual closure hypothesis.Journal ofExperimental Psychology: General, 119,276-298.

SNODGRASS, J. G., & MCCULLOUGH, B. (1986). The role of visual sim­ilarity in picture categorization. Journal ofExperimental Psychology:Learning, Memory. & Cognition, 12, 147-154.

SNODGRASS, 1. G., & POSTER, M. (1992). Visual-word recognition thresh­olds for screen-fragmented names ofthe Snodgrass and Vanderwart pic­tures. Behavior Research Methods, Instruments, & Computers, 24, 1-15.

SNODGRASS, J. G., & VANDERWART, M. (1980). A standardized set of260 pictures: Norms for name agreement, image agreement, famil­iarity, and visual complexity. Journal ofExperimental Psychology:Human Learning & Memory, 6, 174-215.

SNODGRASS, J. G., & YUDlTSKY, T. (1996). Naming times for the Snod­grass and Vanderwart pictures. Behavior Research Methods, Instru­ments, & Computers, 28, 516-536.

STELLA, v., & JOB, R. (in press). Frequenza sillabica e frequenza dilemmi della lingua italiana scritta [Syllabic and lemma frequency forwritten Italian]. Giornale Italiano di Psicologia.

VANDERWART, M. (1984). Priming by pictures in lexical decision. Jour­nal ofVerbal Learning & Verbal Behavior, 23, 67-83.

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STANDARDIZED PICTURES 595

NOTES

I. As an alternative to the retrospective method used in the presentcontextto obtain estimates of age of acquisition (AoA), several more di­rect (and arguably more objective) methods may be proposed. For in­stance, Ellis and Morrison (1998) recently reported AoA measures basedon the actual naming performance of children of different ages, whichis probably the best candidate among these new proposals. Our choiceof the particular method described in this section, however, is to bethought ofas constrained by the main focus of the paper, which is to de­vise a direct comparison with previously published work in the norma­tive field. For this reason, we had to commit to the tradition of (I) usingretrospective judgments of AoA, (2) submitting to subjects, in the AoArating session, a verbal representation of the concepts considered (i.e.,words), and (3) stressing, in the instructions, the importance offocus­ing on the concept each word referred to.

2. Part ofthe material presented in the first session was also presentedin the second session. The second session served the purpose of pre­senting new objects that were made available by the time the first ses­sion had already began. In an attempt to counterbalance the number ofitems presented across sessions, new objects were intermixed with ob­jects presented in the first session, whose NA percentage value wasgreater than 90%.

3. In order to apply the method described in the present section, allthe scores (reported in Appendix B) were transformed into standardz scores. Each correlation coefficient was then transformed into Fish­er's z, and a z test was carried out to estimate the difference between twocorrelation coefficients. Ifthe difference was greater than 1.96, the dif­ference was taken to be significant with .95 probability.

(Continued on next page)

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1.43

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92

.24.

51.

85.

21.

274

293

930.

00

CA

LZA

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52

1.32

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91.

84.

91.

92.

71.

473

876

760.

46

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9)->

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LZE

ST

OC

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52

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14.

91.

84.

91.

93.

72

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0250

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73

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91.

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40.

770

452

970.

89

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s

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2)*

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orts

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30.

852

5.9

1.7

5.1

1.6

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1.1

606

9898

0.00

CA

MIC

IAS

HIR

TC

LO7

32.

622

6.1

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6.7

0.6

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ND

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nti

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63

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ILE

BE

LL-T

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BU

I9

42.

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5.1

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4.5

1.6

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1.6

890

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0.43

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NC

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AT

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83

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31.

981

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100

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NE

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42

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RO

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NG

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MA

M7

30.

951

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CA

NN

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AN

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EA

73

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02

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61.

471

295

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LET

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104

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PP

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AT

CLO

83

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CA

RA

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AP

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73

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1638

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17

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9)->

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RA

ME

LLA

CA

ND

YM

IX9

41.

321

6.3

0.8

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0.6

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100

100

0.00

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RC

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ICH

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EV

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83

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RO

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31.

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100

100

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83

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971

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76

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RR

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EE

LBA

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VE

H8

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100

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83

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42

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CA

SC

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52

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210

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00

CA

SS

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83

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83.

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80.

965

993

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22

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tinu

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NG

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HC

AT

LET

SY

LF

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AM

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NA

CA

H

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TA

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HE

ST

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RU

83

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83.

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292

786

860.

55

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ST

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TLE

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I8

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VA

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73

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82.

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RN

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CLO

83

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94.

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5267

910.

731

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VO

DE

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MA

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TR

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CU

MB

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VE

G8

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HU

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HS

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62

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KS

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go

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52

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tinu

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SY

LF

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TY

PS

OA

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83

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73

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93

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CO

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73

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574

100

100

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CO

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83

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84.

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656

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105

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73

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52

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22.

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34.

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11.

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42

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62

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100

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52

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83.

41.

82.

91.

373

593

930.

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83

1.79

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11.

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33.

11.

411

8021

211.

18

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73

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81.

43.

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272

298

980.

00

GA

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TE

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22.

292

6.4

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2.5

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673

9393

0.27

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OF

AN

OC

AR

NA

TIO

NF

LO8

41.

992

5.5

1.3

5.4

1.5

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1.7

1145

4545

0.49

1->

fiore

(24)

->flo

wer

GA

TIO

CA

TM

AM

52

2.58

16.

51.

66.

11.

22.

01.

167

395

950.

00

GH

IAN

DA

AC

OR

NF

RU

72

0.70

23.

92.

12.

71.

64.

31.

410

6069

690.

43

->no

ce(1

7)->

wal

nut

GIA

CC

AJA

CK

ET

CLO

62

2.47

14.

51.

96.

40.

73.

31.

882

198

980.

00

GIR

AF

FA

GIR

AF

FE

MA

M7

31.

002

4.0

1.9

4.8

1.8

2.9

1.5

559

9898

0.00

GIR

AS

OLE

SU

NF

LOW

ER

FLO

84

1.56

26.

11.

14.

91.

73.

71.

586

369

690.

48

->fio

re(5

)->

flow

er->

mar

gher

ita(7

)->

dais

y

GO

ND

OLA

GO

ND

OLA

VE

H7

31.

182

3.7

2.1

2.2

1.7

4.8

2.0

835

7979

0.43

->ca

noa

(5)

->ca

noe

->na

ve(5

)->

ship

GO

NN

AS

KIR

TC

LO5

22.

171

4.0

2.1

5.7

1.6

2.5

1.1

738

9898

0.00

GR

AT

IAC

IEL

OS

KY

SC

RA

PE

RB

UI

114

1.66

23.

71.

46.

11.

64.

91.

070

395

950.

00

GR

AT

IUG

IAG

RA

TE

RH

OU

93

0.70

26.

90.

45.

21.

74.

21.

568

095

950.

00

GU

AN

TI

GLO

VE

SC

LO6

22.

111

5.3

1.8

4.3

1.5

3.1

1.3

639

100

100

0.00

GU

AN

TO

GLO

VE

CLO

62

1.76

25.

31.

84.

31.

52.

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262

010

010

00.

00

en ~ Z ~ ::0 '=' N tTl '=' '"t

:l n >-l c:::: ~ en 0'

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AP

PE

ND

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(Con

tinu

ed)

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NG

LIS

HC

AT

LET

SY

LF

RO

SF

AM

SO

TY

PS

OA

oAS

OR

TN

AC

AH

GU

FO

OW

LB

IR4

21.

902

4.1

1.9

4.2

2.0

4.1

1.5

708

9090

0.33

->ci

vetta

(10)

->lit

tleow

l

IGLO

OIG

LOO

BU

I5

21.

111

1.9

1.2

2.9

2.1

5.7

1.3

872

9393

0.00

IMB

UT

OF

UN

NE

LH

OU

63

1.38

26.

70.

64.

31.

93.

81.

282

610

010

00.

00

IPP

OP

OT

AM

OH

IPP

OP

OT

AM

US

MA

M10

50.

952

2.9

1.8

3.9

2.1

4.0

1.2

868

8383

0.22

->rin

ocer

onte

(5)

->rh

inoc

eros

KIW

IK

IWI

FR

U4

21.

382

5.5

1.8

5.5

1.6

5.3

1.8

970

6969

0.43

->m

ango

(5)

->m

ango

->pe

sca

(5)

->pe

ach

-LA

MP

AD

ALA

MP

FO

R7

32.

192

5.5

1.3

5.3

1.3

2.9

1.0

717

8198

0.43

->a

ba

t-jo

ur

(17)

*->

lam

psha

de

LEO

NE

LIO

NM

AM

53

2.61

14.

32

.35.

91.

12.

40.

868

310

010

00.

00

LE

TIO

BE

DF

OR

52

3.01

16.

70.

66.

70.

61.

80.

462

010

010

00.

00

L1B

RE

RIA

BO

OK

CA

SE

FO

R8

42.

542

6.3

0.8

6.6

0.5

4.3

1.8

891

4562

1.58

->ar

mad

io(1

7)->

war

drob

e

->m

enso

la(5

)->

cons

ole

->sc

affa

le(1

0)*

->sh

elf

->sc

affa

li(7

)*->

shel

ves

->sc

rivan

ia(1

0)->

desk

L1M

ON

ELE

MO

NF

RU

63

2.10

15.

11.

94.

92.

02.

71.

210

6495

950.

00

LOC

OM

OT

IVA

LOC

OM

OT

IVE

VE

H10

51.

792

5.7

1.8

4.7

1.7

3.9

1.2

835

3131

0.39

->tr

eno

(67)

@->

trai

n

MA

GLI

ON

ES

WE

AT

ER

CLO

83

1.62

26.

70.

66.

50.

72.

61.

261

193

930.

00

MA

IALE

PIG

MA

M6

32.

081

5.4

1.5

4.5

1.6

2.5

1.2

778

9898

0.00

MA

ND

OLI

NO

MA

ND

OLI

NIN

S9

40.

902

3.5

1.6

4.1

1.8

6.3

1.4

1149

6262

0.51

->ch

itarr

a(2

6)->

guita

r

MA

NE

TIE

HA

ND

CU

FF

SM

IX7

31.

581

2.1

1.8

1.4

0.8

5.1

1.1

761

9898

0.00

MA

PP

AM

AP

MIX

52

2.18

23.

92.

06.

41.

15.

71.

380

271

850.

40

->ca

rtin

a(1

4)*

-sm

ap

MA

PP

AM

ON

DO

GLO

BE

MIX

104

0.95

15.

71.

73.

22.

14.

91.

573

798

980.

00

MA

RG

HE

RIT

AD

AIS

YF

LO10

42.

182

6.4

1.6

6.8

0.4

2.8

1.6

920

7474

0.40

->fio

re(1

4)->

flo

we

r

MA

RT

ELL

OH

AM

ME

RM

IX8

31.

621

3.9

2.0

6.3

1.0

3.7

1.2

672

9595

0.00

0\ o ,l::. o t"I'l

r-' r:: 1:; ;::) ~ r-' ~ .d :> z o '- o t:C

AP

PE

ND

IXA

(Con

tinu

ed)

ITA

LIA

NE

NG

LIS

HC

AT

LET

SY

LF

RO

SF

AM

SO

TY

PS

OA

oAIS

ORT

NA

CA

lH

MA

ZZ

AS

TA

FF

WE

A5

21.

712

1.4

0.8

2.7

2.1

5.3

1.9

933

6060

0.33

->ar

ma

(10)

->w

ea

po

n

ME

LAA

PP

LEF

RU

42

2.27

16.

31.

66.

90.

31.

70.

5no

100

100

0.00

ME

LAN

ZA

NA

EG

G-P

LA

NT

VE

G9

41.

282

6.4

1.1

5.3

1.8

4.1

1.8

954

9595

0.00

ME

LOG

RA

NO

PO

ME

GR

AN

AT

EF

RU

94

0.70

24.

42

.34.

52.

04.

71.

515

8764

640.

27

->ci

polla

(7)

->on

ion

ME

LON

EM

ELO

NF

RU

63

1.46

24.

31.

84.

22.

13.

91.

113

8043

431.

03

->an

guria

(10)

->w

ater

mel

on

->co

com

ero

(5)

->w

ater

mel

on

->fr

utto

(5)

->fr

uit

->zu

cca

(7)

->pu

mpk

in

ME

ST

OLO

LAD

LEH

OU

73

0.95

26.

90.

46.

21.

44.

11.

085

438

380.

88

->cu

cchi

aino

(12)

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alls

poon

->cu

cchi

aio

(48)

@->

spoo

n

MIT

RA

LIG

HT

MA

CH

INE

GU

NW

EA

52

2.03

21.

30.

66.

21.

65.

31.

310

3717

171.

32

->fu

cile

(31)

@->

rifle

->m

itrag

liatr

ice

(7)

->m

achi

negu

n

->pi

stol

a(3

1)@

->g

un

MIT

RA

GLI

AT

RIC

EM

AC

HIN

EG

UN

WE

A14

51.

462

1.2

0.6

5.0

2.3

5.6

1.2

1017

4040

1.18

->bi

noco

lo(1

0)->

bino

cula

r

->m

acch

ina

foto

graf

ica

(7)

->ca

mer

a

->m

itrag

lietta

(5)

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tlem

achi

negu

n

->te

lesc

opio

(12)

->te

lesc

ope

MO

NG

OLF

IER

AF

IRE

-BA

LLO

ON

VE

H11

41.

232

3.1

1.8

1.7

0.8

5.2

1.1

827

8383

0.00

MO

RA

MU

LBE

RR

YF

RU

73

1.23

24.

51.

85.

11.

84.

41.

715

3257

571.

16

->la

mpo

ne(7

)->

rasp

berr

y

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irtill

o(5

)->

bilb

erry

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es(5

)->

red

curr

ent

->uv

a(1

9)->

grap

es

MO

TO

SC

AF

OM

OT

OR

BO

AT

VE

H9

41.

522

4.9

1.6

3.7

1.4

4.3

1.5

1073

3333

0.91

I->

barc

a(5

5)@

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at

->na

ve(5

)->

ship

->za

ttera

(5)

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ft

VJ > Z ~ § N t'Ii o '"I::l n -l c::: ~ VJ

0­ o VI

AP

PE

ND

IXA

(Con

tinu

ed)

ITA

LIA

NE

NG

LIS

HC

AT

LET

SY

LF

RQ

SF

AM

SO

TY

PS

OA

oAS

ORT

NA

CA

HM

UC

CA

CO

WM

AM

52

1.70

16.

11.

25.

51.

62.

41.

287

095

950.

00

MU

LIN

OW

IND

MIL

LB

UI

63

2.10

22.

01.

22.

91.

93.

91.

4no

9595

0.00

NA

CC

HE

RE

CA

ST

AN

ET

SIN

S8

30.

702

3.5

2.2

2.8

1.4

5.6

1.3

1267

8181

0.22

->m

arac

as(5

)->

mar

acas

NA

RC

ISO

NA

RC

ISS

US

FLO

73

1.48

25.

01.

65.

91.

15.

90.

911

505

50.

67

->fio

re(1

9)->

flow

er

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s(5

)->

iris

NA

VE

SH

IPV

EH

42

2.79

24.

52

.34.

51.

63.

01.

274

793

930.

27

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rca

(7)

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at

NIN

FE

AW

AT

ER

LILY

FC

O6

30.

902

4.3

2.4

3.7

2.3

6.2

1.7

1853

3333

0.54

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nfa

(7)

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mph

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chid

ea(7

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orch

id

NO

CE

WA

LNU

TF

RU

42

2.30

15.

11.

64.

61.

63.

41.

482

295

950.

00

OC

AG

OO

SE

BIR

32

2.65

25.

01.

73.

41.

23.

11.

397

471

780.

90

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atra

(12)

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ck

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pera

(7)"

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slin

g

->uc

cello

(7)

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rd

OLi

ER

AO

ILC

RU

ET

RE

C6

30.

012

3.3

1.6

3.6

1.7

5.5

1.5

1624

3150

1.73

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etie

ra(5

)"->

vine

garc

ruet

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polla

(14)

*->

crue

t

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ccet

ta(7

)->

smal

lbot

tle-~

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ttigl

ia(1

2)->

bottl

e

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raffa

(5)

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tche

r

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so(7

)->

vase

OM

BR

ELL

OU

MB

RE

LLA

MIX

83

1.85

16.

11.

02.

91.

52.

91.

359

298

980.

00

OR

CH

IDE

AO

RC

HID

FLO

84

1.34

24.

11.

94.

32

.35.

71.

518

7417

171.

07

->fio

re(1

9)->

flo

we

r

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glio

(5)

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y

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s(1

4)->

iris

OR

EC

CH

INI

EA

RR

ING

SM

IX9

41.

801

4.3

1.5

5.7

1.7

4.0

1.8

976

9393

0.00

OR

GA

NO

OR

GA

NIN

S6

32.

432

4.3

2.1

3.9

2.0

5.3

1.9

895

8686

0.22

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anof

orte

(5)

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ano

OR

SO

BE

AR

MA

M4

22.

361

3.6

2.0

5.2

1.7

2.8

1.4

796

9898

0.00

0\ o 0\ o tTl

l' r: f; D ~ l' ~ .<:5 :;I> z o ...... o ttl

AP

PE

ND

IXA

(Co

nti

nu

ed

)

ITA

LIA

NE

NG

LIS

HC

AT

ILE

TS

YL

FR

OlS

IF

AM

SO

TY

PS

OA

oAI

SO

RT

IN

AC

Al

HI

PA

GO

DA

PA

GO

DA

BU

I6

31.

182

1.8

1.5

3.1

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7.5

1.1

98

924

241.

68

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bero

dina

tale

(10)

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ristm

astr

ee

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sa(5

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hous

e

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stru

zion

e(5

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build

ing

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pola

(5)

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pola

->te

mpi

o(5

)->

tem

ple

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tto(7

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roof

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rre

(5)

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we

r

PA

LMA

PA

LMT

RE

EF

LO5

22.

182

4.1

2.1

4.5

2.1

5.1

1.8

75

095

950.

00

PA

NN

OC

CH

IAC

OR

NV

EG

103

1.08

25.

41.

63.

31.

64.

41.

682

867

880.

47

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ais

(21)

·->

pani

cle

PA

NT

ALO

NI

PA

NT

SC

LO9

42.

391

6.8

0.4

6.7

0.5

2.3

0.6

676

9398

0.22

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lzon

i(5)

·->

trou

sers

PA

NT

OF

OLA

SLI

PP

ER

CLO

94

0.70

24.

72.

13.

01.

63.

31.

491

131

551.

02

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abat

ta(2

4)"

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ippe

r

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arpa

(40)

@->

shoe

PA

NT

OF

OLE

SLI

PP

ER

SC

LO9

41.

732

4.7

2.1

3.0

1.6

3.3

1.4

1065

4265

1.01

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abat

te(2

3)·

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ippe

rs

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arpe

(30)

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oes

PA

PA

VE

RO

PO

PP

YF

LO8

41.

112

5.6

1.7

6.1

1.3

4.1

1.6

1241

4848

0.71

->fio

re(2

4)->

flo

we

r

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rofa

no(5

)->

cam

atio

n

PA

PP

AG

ALL

OP

AR

RO

TB

IR10

41.

671

4.9

1.7

5.3

1.5

3.5

1.1

1095

9090

0.27

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cello

(7)

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rd

PA

VO

NE

PE

AC

OC

KB

IR6

31.

341

4.1

2.0

3.6

1.7

4.5

1.2

908

9898

0.00

PE

CO

RA

SH

EE

PM

AM

63

1.89

15.

31.

34.

52.

02.

81.

110

4598

980.

00

PE

LLIC

AN

OP

ELI

CA

NB

IR9

40.

302

3.7

1.9

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1074

4545

0.85

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cogn

a(7

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stor

k

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nico

ttero

(5)

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min

go

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cello

(12)

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rd

PE

NN

ELL

OB

RU

SH

MIX

83

1.83

13.

21.

75.

11.

53.

91.

510

5093

930.

22

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azzo

la(5

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clot

hes-

brus

h

PE

NT

OLA

PO

TH

OU

73

1.95

27.

00.

06.

70.

52.

70.

874

498

980.

00

r:n ~ Z §2 ;;tl tl N t'Ii

tl ""C n '""':l c: G; r:n CJ'

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AP

PE

ND

IXA

(Con

tinu

ed)

ITA

LIA

NE

NG

LIS

HC

AT

LET

SY

LF

RO

SF

AM

SO

TY

PS

OA

oAS

OR

TN

AC

AH

PE

PE

RO

NE

PE

PP

ER

VE

G8

41.

561

6.4

1.1

5.6

1.5

4.3

1.3

1103

9595

0.00

PE

RA

PE

AR

FR

U4

21.

751

5.8

1.8

6.7

0.6

2.1

0.6

696

100

100

0.00

PE

SC

AP

EA

CH

FR

U5

22.

122

6.2

1.1

6.1

1.4

2.4

0.7

1144

8888

0.27

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bico

cca

(7)

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ricot

PIA

NE

TA

PLA

NE

TM

IX7

32.

422

3.5

1.8

6.9

0.4

5.9

1.7

935

4578

0.96

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ondo

(5)

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orl

d

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telli

te(5

)->

sate

llite

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tum

o(3

3)*

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tum

PIA

NO

FO

RT

EP

IAN

OIN

S10

42.

312

5.4

2.1

5.1

1.6

4.3

1.5

674

100

100

0.00

PIC

CH

IOW

OO

DP

EC

KE

RB

IR7

20.

702

4.3

2.0

5.6

1.8

4.3

1.2

1140

8686

0.48

->co

librl

(5)

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mm

ing-

bird

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cello

(7)

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rd

PIC

CIO

NE

PIG

EO

NB

IR8

31.

432

5.7

1.9

4.7

2.1

3.7

1.3

1280

4555

1.15

\->

colo

mba

(12)

->do

ve

->co

lom

bo(1

0)*

->do

ve

->uc

cello

(19)

->bi

rdP

IGIA

MA

PA

JAM

AS

CLO

73

1.51

16.

11.

42.

51.

52.

30.

784

898

980.

00

PIN

GU

INO

PE

NG

UIN

BIR

83

1.26

13.

62

.23.

22

.33.

31.

490

495

950.

00

PIP

AP

IPE

MIX

42

1.84

12.

72

.34.

31.

93.

61.

773

710

010

00.

00

PIR

AM

IDE

PY

RA

MID

BU

I8

42.

061

2.6

1.5

2.3

1.6

4.7

1.0

878

9898

0.00

PIS

CIN

AS

WIM

MIN

GP

OO

L-

BU

I7

32.

351

4.6

1.2

4.7

1.9

3.4

1.0

1041

9393

0.00

PIS

ELL

IP

EA

SV

EG

73

1.81

26.

21.

16.

70.

63.

31.

310

1071

710.

76

->fa

giol

i(5)

->be

ans

->fa

giol

ini(

5)->

runn

ers

->fa

giol

o(1

0)->

bean

PIS

TO

LAG

UN

WE

A7

32.

501

1.7

1.6

6.7

0.6

3.3

1.0

662

100

100

0.00

PO

LTR

ON

AA

RM

CH

AIR

FO

R8

32.

581

6.3

0.7

6.2

1.7

2.7

0.7

742

8888

0.43

->di

vano

(5)

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uch

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dia

(5)

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air

PO

MO

DO

RO

TO

MA

TO

VE

G8

42.

251

6.7

1.0

2.7

1.7

2.7

0.8

1025

100

100

0.00

PO

NT

EB

RID

GE

BU

I5

22.

821

3.9

1.7

3.2

2.2

3.5

0.9

1050

9393

0.00

PO

RR

OLE

EK

VE

G5

21.

462

3.6

2.1

5.1

1.9

6.5

1.4

1758

22

1.49

->ba

ston

e(7

)->

stic

k

0\ o 00 o rn r­ r:: f; ,Q ~ r- ~ ~ ~ o .....

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AP

PE

ND

IXA

(Co

nti

nu

ed)

ITA

LIA

NE

NG

LIS

HC

AT

LET

SY

LF

RO

SF

AM

SO

TY

PS

OA

oAS

OR

TN

AC

AH

->le

gno

(5)

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ece

ofw

ood

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mo

(45)

@->

bran

ch

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mos

cello

(5)

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tlebr

anch

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dano

(7)

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lery

PO

RT

ICO

CO

LON

NA

DE

SU

I7

31.

792

4.7

1.9

1.7

1.1

5.1

2.1

1081

5593

0.87

->ar

chi(

26)*

->ar

ches

->po

rtic

ato

(12)

*->

arca

de

PO

llO

WE

LLS

UI

52

2.18

13.

11.

86.

40.

63.

91.

280

695

950.

00

PU

GN

ALE

DA

GG

ER

WE

A7

31.

582

1.5

1.0

5.1

1.4

4.2

1.4

733

2929

0.37

->co

ltello

(69)

@->

knife

RA

DIO

RA

DIO

MIX

52

2.98

16.

90.

45.

31.

43.

31.

177

810

010

00.

00I

RA

PA

TU

RN

IPV

EG

42

0.70

24.

12

.44.

91.

95.

31.

614

5050

501.

32

->ci

polla

(10)

->on

ion

->pa

tata

(17)

->po

tato

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vane

llo(1

0)->

radi

sh

->ve

rdur

a(5

)->

vege

tabl

e

RA

SO

IOR

AZ

OR

MIX

63

1.67

23.

42

.43.

42.

15.

11.

610

5762

621.

16

->an

tenn

a(5

)->

ante

nna

->la

met

ta(1

0)->

razo

r-b

lad

e

->m

arte

llo(1

4)->

ham

mer

->ra

stre

llo(5

)->

rake

RA

ST

RE

LLO

RA

KE

MIX

93

0.95

13.

52.

05.

21

.73.

91.

487

093

930.

00

RIS

ES

RE

DC

UR

RE

NT

FR

U5

20.

702

5.3

2.2

5.2

1.6

5.5

2.1

612

1212

0.32

->uv

a(7

4)@

->gr

apes

RIN

OC

ER

ON

TE

RH

INO

CE

RO

SM

AM

115

1.77

22.

72.

03.

71.

94.

61.

275

093

930.

27

->ip

popo

tam

o(7

)->

hipp

opot

amus

RO

ND

INE

SW

ALL

OW

SIR

73

1.36

26.

20.

76.

70.

64.

01.

283

493

930.

00

RO

SA

RO

SE

FLO

42

2.61

26.

90.

36.

70.

82.

51.

160

710

010

00.

00

RO

ULO

TT

ET

RA

ILE

RV

EH

83

1.51

25.

11.

83.

11.

65.

31.

586

879

860.

54

->ca

mp

er

(7)

->ca

mpe

r

->ca

rava

n(7

)*->

cara

van

RU

SIN

ET

TO

FA

UC

ET

MIX

94

1.79

16.

70.

75.

52.

03.

11.

578

193

930.

00

SA

ND

All

SA

ND

ALS

CLO

73

1.63

13.

92.

13.

91.

63.

01.

211

0283

830.

58

en ~ Z ~ ;:0 o N rn c '" n --l c: ;:0 tri en 0\ o \0

AP

PE

ND

IXA

(Con

tinu

ed)

ITA

LIA

NE

NG

LIS

HC

AT

LET

SY

LF

RQ

SF

AM

SO

TY

PS

OA

oAS

OR

TN

AC

AH

->ci

abat

te(1

2)->

slip

pers

->sc

arpa

(5)

->sh

oes

SA

ND

ALO

SA

ND

AL

CLO

73

1.18

23.

92.

13.

91.

64.

11.

982

279

790.

58->

ciab

atta

(12)

->sl

ippe

r

->sc

arpa

(5)

->sh

oe

SA

SS

OF

ON

OS

AX

OP

HO

NE

INS

94

0.85

24.

51.

95.

71.

35.

91.

691

274

740.

66

->cl

arin

etto

(10)

->cl

arin

et

->tr

omba

(10)

->tr

umpe

t

SC

ALI

NA

TA

ST

AIR

WA

YB

UI

94

1.48

25.

31.

83.

31.

32.

00.

869

540

991.

03->

scal

a(2

6)·

->st

air

->sc

ale

(33)

·->

stai

rs

SC

AR

PA

SH

OE

CLO

62

1.97

25.

61.

66.

11.

42.

10.

862

710

010

00.

00

SC

AR

PE

SH

OE

SC

LO6

22.

741

5.6

1.6

6.1

1.4

1.9

0.7

684

100

100

0.00

SC

AT

OLA

BO

XR

EC

73

2.46

25.

31.

25.

51.

82.

10.

762

510

010

00.

00

SC

IAR

PA

SC

AR

FC

LO7

21.

791

6.3

0.8

5.9

1.2

2.5

1.0

717

100

100

0.00

SC

OIA

TT

OLO

SQ

UIR

RE

LM

AM

104

1.04

24.

51.

74.

11.

82.

91.

170

593

930.

00S

CO

PA

BR

US

HH

OU

52

1.40

26.

90.

36.

01.

52.

71.

261

198

980.

00

SC

R/V

AN

IAD

ES

KF

OR

94

2.48

26.

21.

15.

71.

73.

91.

483

383

830.

33->

tavo

lo(1

0)->

tabl

e

SE

CC

HIO

BU

CK

ET

RE

C7

21.

482

4.8

1.4

6.1

0.8

2.8

1.1

761

9595

0.00

SE

DA

NO

CE

LER

YV

EG

63

1.63

26.

01.

34.

92.

14.

51.

210

7176

760.

48

->fin

occh

io(7

)->

fenn

el

->pr

ezze

mol

o(5

)->

pars

ley

SE

DIA

CH

AIR

FO

R5

22.

411

6.8

0.4

6.3

1.0

2.0

0.8

555

100

100

0.00

SG

AB

ELL

OS

TO

OL

FO

R8

31.

652

4.7

1.9

4.1

1.6

3.1

1.5

736

7979

0.43

->se

dia

(5)

->ch

air

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ggio

lino

(5)

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at

SLi

TT

AS

LED

VE

H6

21.

512

3.3

2.1

1.9

1.1

3.3

1.7

849

8888

0.00

SO

MM

ER

GIB

ILE

SU

BM

AR

INE

VE

H12

51.

042

1.8

1.7

2.1

1.4

5.5

1.3

1270

6471

0.60

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rigib

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0)->

dirig

ible

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ttom

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e

SP

AD

AS

WO

RD

WE

A5

22.

351

1.7

1.2

4.7

2.0

2.9

1.1

821

9595

0.00

ST

IVA

LEB

OO

TC

LO7

31.

202

4.3

2.0

4.3

2.0

3.5

1.4

651

100

100

0.00

0\ ..... o o tTl

r r:: ~ to ~ r ~ .<3 >­ z o '-< o ttl

AP

PE

ND

IXA

(Con

tinu

ed)

ITA

LIA

NE

NG

LIS

HC

AT

LET

SY

LF

RQ

SF

AM

SO

TY

PS

OA

oAS

OR

TN

AC

AH

ST

IVA

LIB

OO

TS

CLO

73

2.08

14.

32.

04.

32.

03.

30.

870

910

010

00.

00S

TR

UZ

ZO

OS

TR

ICH

BIR

72

1.28

23.

31.

92.

91.

24.

91.

110

1283

830.

00

SV

EG

LIA

CLO

CK

MIX

72

1.93

16.

90.

36.

01.

13.

61.

271

090

900.

27

->or

olog

io(7

)->

wat

ch

TA

MB

UR

OD

RU

MIN

S7

31.

861

4.3

1.7

5.1

1.4

3.3

1.3

790

9595

0.00

TA

NIC

AJE

RR

YC

AN

RE

C6

30.

702

4.1

1.8

4.5

1.9

6.0

1.3

853

7676

0.22

->bo

tte(5

)->

barr

el

TA

VO

LOT

AB

LEF

OR

63

2.99

16.

90.

36.

60.

52.

10.

760

998

980.

00

TA

VO

LOZ

ZA

PA

LE

TIE

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94

1.52

23.

11.

83.

32.

15.

91.

411

5550

500.

49

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glie

re(2

4)->

cho

pp

ing

-bo

ard

TA

ZZ

AC

UP

HO

U5

22.

172

7.0

0.0

6.3

1.0

2.5

1.1

685

9090

0.00

TE

IER

AT

EA

PO

TR

EC

63

1.32

24.

71.

63.

71.

65.

11.

585

779

790.

48

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raffa

(7)

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r

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se(5

)->

vase

TE

NA

GLI

AP

INC

ER

SM

IX8

31.

342

3.7

1.8

6.0

1.4

5.7

1.6

815

5252

0.53

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nza

(33)

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iers

TE

ND

AT

EN

TM

IX5

21.

951

3.5

2.3

1.8

1.0

3.4

1.1

925

9398

0.22

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epee

(5)"

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epee

TIG

RE

TIG

ER

MA

M5

22.

172

4.1

2.4

5.7

1.6

3.0

1.4

660

9595

0.00

TO

PO

MO

US

EM

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42

2.59

15.

51.

62.

51.

42.

61.

165

098

980.

00

TO

RR

ET

OW

ER

BU

I5

22.

581

3.9

1.4

4.7

1.5

3.9

1.4

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9090

0.27

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stel

lo(7

)->

cast

le

TR

AM

TR

AM

CA

RV

EH

41

2.13

24.

91.

84.

62.

04.

92.

199

367

670.

51

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(26)

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ain

TR

AP

AN

OD

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2.1

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0.9

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TR

AT

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RA

CT

OR

VE

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31.

201

5.5

1.9

3.1

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TR

EN

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52

2.78

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51.

46.

11.

22.

51.

183

610

010

00.

00

TR

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LEV

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83

1.08

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31.

92.

71.

92.

21.

483

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00

TR

OM

BA

TR

UM

PE

TIN

S6

21.

881

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2.1

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1.7

3.4

1.4

814

9595

0.00

TU

CA

NO

TO

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AN

BIR

63

0.85

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32

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92.

06.

12.

113

7160

600.

54

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(7)

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TU

LIP

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OT

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PF

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40.

852

6.1

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6.1

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PE

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ed)

ITA

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NG

LIS

HC

AT

LET

SY

LF

RO

SF

AM

SOT

YP

SOA

oASO

RT

NA

CA

H

->fio

re(5

)->

flo

we

r

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sa(7

)->

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UV

AG

RA

PE

SF

RU

32

2.28

26.

41.

56.

30.

92.

61.

25

n93

100

0.27

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h

VE

ST

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DR

ES

SC

LO7

32.

412

3.5

2.1

5.7

1.8

2.0

0.8

700

8397

0.40

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ne

nt

VIO

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TF

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21.

762

4.6

2.2

5.9

1.4

3.6

1.7

1196

2934

1.02

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ino

(12)

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en

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re(1

7)->

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er

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little

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et

VIO

LIN

OV

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S7

32.

152

4.5

1.6

6.3

1.0

4.5

0.9

763

8686

0.33

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itarr

a(1

0)->

guita

r

ZA

INO

BA

CK

PA

CK

MIX

52

1.93

24.

81.

76.

60.

74.

51.

213

0790

900.

00

ZA

TIE

RA

RA

FT

VE

H7

31.

232

2.8

2.0

1.3

0.6

4.8

0.9

894

9393

0.00

ZE

BR

AZ

EB

RA

MA

M5

21.

201

3.8

1.7

4.8

1.9

3.6

1.4

761

9595

0.22

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(5)

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CC

AP

UM

PK

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EG

52

1.41

25.

71.

54.

72.

04.

81.

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586

860.

22

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(5)

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mat

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0\ - N o tTl

t""' r:: ~ 10 ~ t""' ~ P ~ o ..... o l:l:l

AP

PE

ND

lXB

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toft

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mul

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mm

on

toO

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hat

ofS

nodg

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and

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der

war

t(19

80)

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the

Tex

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LA

NG

UA

GE

FA

MIL

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AG

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Ms

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fH

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Ha

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Hs

FR

QF

RQ

aF

RQ

fF

RQ

sF

ISA

RM

ON

ICA

AC

CO

RD

ION

4.20

2.17

2.15

1.63

1.55

4.80

5.65

6.24

3.08

0.22

0.43

0.18

0.00

0.16

1.38

0.30

0.73

0.95

AE

RE

OA

IRP

LA

NE

5.73

3.22

3.78

2.63

2.43

3.47

4.03

3.49

1.92

0.00

0.69

1.77

0.29

0.00

2.86

1.08

1.71

1.91

ME

LAA

PP

LE6.

334.

323.

984.

404.

331.

732.

152.

551.

460.

000.

000.

160.

000.

022.

271.

001.

501.

38C

AR

CIO

FO

AR

TIC

HO

KE

6.00

3.30

2.29

2.13

2.44

4.07

5.07

6.28

3.04

0.00

0.00

1.54

0.23

0.30

1.81

0.00

0.39

0.48

AS

PA

RA

GO

AS

PA

RA

GU

S5.

672.

602.

682.

372.

635.

136.

256.

033.

190.

580.

641.

270.

160.

580.

700.

300.

420.

30A

SC

IAA

XE

2.80

2.37

2.28

1.57

1.86

5.00

5.95

4.97

2.64

0.85

1.48

0.53

0.00

0.04

1.26

1.11

1.01

1~~

BA

NA

NA

BA

NA

NA

6.00

4.05

3.65

3.87

3.84

2.33

2.53

2.76

1.58

0.00

0.00

0.00

0.00

0.00

1.68

0.70

0.57

0.85

BO

TT

EB

AR

RE

L3.

802.

172.

021.

272.

163.

874.

805.

373.

150.

000.

360.

000.

791.

011.

841.

401.

040.

90O

RS

OB

EA

R3.

601.

821.

981.

601.

512.

803.

883.

561.

620.

000.

320.

530.

000.

042.

361.

761.

051.

67LE

TT

OB

ED

6.73

4.77

4.72

4.93

4.78

1.80

2.15

2.42

1.24

0.00

0.48

0.00

0.51

0.04

3.01

2.11

2.31

2.62

CIN

TU

RA

BE

LT5.

934.

304.

124.

134.

433.

673.

953.

952.

420.

221.

310.

160.

410.

132.

121.

481.

411.

67B

ICIC

LE

TT

AB

ICY

CLE

6.87

3.80

3.78

3.37

3.61

2.27

3.45

3.74

1.80

0.22

0.00

0.53

0.41

0.18

2.45

0.78

0.73

1.41

ST

IVA

LEB

OO

T4.

333.

803.

383.

734.

083.

533.

553.

752.

040.

001.

440.

690.

400.

061.

201.

151.

351.

00B

OT

TIG

LIA

BO

TT

LE6.

334.

603.

724.

203.

881.

872.

923.

581.

920.

000.

000.

280.

000.

002.

401.

891.

622.

06S

CA

TO

LA

BO

X5.

333.

722.

882.

973.

492.

133.

702.

691.

650.

000.

000.

800.

260.

002.

461.

851.

732.

08S

CO

PA

BR

US

H6.

934.

003.

804.

233.

982.

673.

383.

081.

770.

000.

270.

880.

150.

181.

401.

651.

011.

23C

AN

DE

LA

CA

ND

LE6.

333.

423.

083.

604.

842.

733.

784.

101.

960.

000.

000.

000.

000.

001.

981.

281.

281.

74C

AN

NO

NE

CA

NN

ON

1.67

1.70

1.52

1.07

n.a.

4.60

4.55

n.a.

3.08

0.00

0.00

0.49

0.16

n.a.

1.98

0.90

1.6~_

n.a.

---

AU

TO

MO

BIL

EC

AR

7.00

4.27

4.70

4.53

4.43

2.80

3.50

2.73

1.40

1.34

1.48

1.08

0.15

0.06

2.60

2.44

2.08

2.48

CA

RO

TA

CA

RR

OT

6.73

3.55

3.55

3.90

3.33

2.93

3.80

3.16

1.58

0.00

0.00

0.00

0.00

0.02

1.72

0.30

0.62

1.04

GA

TT

OC

AT

6.47

3.85

4.22

3.63

3.06

2.00

2.45

2.50

1.35

0.00

0.16

0.00

0.00

0.00

2.58

1.38

1.65

2.15

SE

DA

NO

CE

LE

RY

6.00

3.15

3.40

2.0~_~.73

4.53

5.35

5.00

3.46

0.48

1.35

0.83

0.65

1.27

1.63

0.70

0.21

0.30

~_IA

CH

AIR

6.80

4.57

4.58

4.93

2.10

2.00

2.65

2.92

1.38

0.00

0.00

0.00

0.00

0.00

2.41

1.83

1.92

2.12

8!:

'EG

IAC

HE

RR

Y6.

133.

503.

383.

133.

062.

933.

173.

792.

000.

370.

160.

520.

290.

09~04

0.85

0.90

n.a~_

GA

LL

INA

CH

ICK

EN

5.80

2.67

2.42

2.30

2.45

2.40

3.10

3.13

1.50

0.00

0.48

1.35

0.47

0.09

2.10

1.58

1.36

1.58

CH

IES

AC

HU

RC

H5.

203.

253.

382.

973.

492.

873.

253.

852.

270.

000.

000.

440.

150.

003.

222.

542.

252.

34S

VE

GL

IAC

LO

CK

6.93

4.17

4.38

4.73

3.82

3.60

3.72

3.47

2.69

0.27

0.78

0.16

1.01

0.10

1.93

1.32

1.17

2.16

--

PA

NN

OC

CH

IAC

OR

N5.

433.

123.

503.

102.

434.

405.

303.

502.

600.

471.

480.

880.

560.

211.

081.

540.

931.4~_

DIV

AN

OC

OU

CH

6.67

4.35

4.40

4.40

4.45

2.47

4.00

3.63

2.16

0.00

0.16

0.92

0.34

0.28

2.22

1.11

1.00

1.83

MU

CC

AC

OW

6.13

2.70

2.42

2.63

3.63

2.40

3.10

3.11

1.60

0.00

0.35

0.44

0.00

0.00

1.70

1.48

1.56

1.40

TA

ZZ

AC

UP

7.00

4.40

4.40

4.83

3.75

2.47

3.78

2.68

2.16

0.00

1.35

0.44

0.15

0.09

2.17

1.66

1.35

1.67

CE

RV

OD

EE

R4.

930.

752.

221.

871.

513.

934.

853.

982.

880.

330.

751.

440.

290.

561.

991.

150.

680.

95S

CR

IVA

NIA

DE

SK

6.20

4.47

4.32

4.60

4.80

3.87

5.20

3.92

2.65

0.33

0.48

0.32

0.00

0.14

2.48

1.82

2.03

1.46

CA

NE

DO

G6.

604.

204.

603.

804.

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872.

052.

231.

190.

000.

000.

000.

000.

002.

781.

882.

092.

35 --

AS

INO

DO

NK

EY

5.47

1.82

1.88

2.07

1.88

2.53

3.78

4.35

2.08

0.74

0.16

0.87

0.00

0.18

1.85

0.30

1.37

1.57

VE

ST

ITO

DR

ES

S3.

533.

603.

623.

403.

762.

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883.

321.

460.

400.

870.

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112.

411.

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tinu

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TA

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UR

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M4.

332.

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601.

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334.

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272.

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350.

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861.

081.

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NA

TR

AD

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K5.

202.

652.

752.

502.

413.

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532.

931.

850.

911.

470.

280.

290.

041.

411.

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151.

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QU

ILA

EA

GLE

4.07

1.80

2.42

1.50

1.82

4.07

4.85

5.08

2.73

0.71

1.07

1.14

0.54

0.12

2.28

0.78

1.09

1.48

ELE

FA

NT

EE

LE

PH

AN

T4.

202.

102.

351.

401.

432.

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723.

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900.

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OR

CH

ET

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671.

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11G

IRA

FF

AG

IRA

FF

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801.

301.

532.

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212.

120.

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320.

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48B

ICC

HIE

RE

GLA

SS

7.00

4.50

4.78

4.97

2.35

1.80

2.15

2.90

1.23

0.00

0.00

0.16

0.00

0.00

2.53

2.00

2.00

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GU

AN

TO

GLO

VE

5.33

3.42

3.38

3.97

3.94

2.93

3.78

3.33

2.00

0.00

0.00

0.16

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0.11

1.76

1.00

1.37

1.40

UV

AG

RA

PE

S6.

363.

923.

653.

602.

312.

603.

173.

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270.

710.

380.

290.

302.

280.

901.

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HIT

AR

RA

GU

ITA

R6.

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473.

582.

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GU

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731.

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681.

531.

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3.33

4.32

4.05

2.81

0.00

0.27

1.09

0.73

0.19

2.50

2.08

1.36

1.71

AR

PA

HA

RP

3.87

1.85

1.88

1.67

1.63

5.73

6.55

6.08

3.42

0.00

0.16

0.00

0.15

0.09

1.40

0.30

0.79

0.70

CA

PP

ELL

OH

AT

2.80

3.52

3.18

2.83

4.67

2.47

3.12

2.90

1.62

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1.75

0.16

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2.58

1.76

1.95

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EL

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AV

ALL

OH

OR

SE

5.67

2.92

3.55

2.63

2.57

2.60

3.03

3.53

1.54

0.00

0.00

0.00

0.00

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2.94

2.07

2.13

2.27

CA

SA

HO

US

E6.

333.

504.

384.

474.

361.

671.

922.

411.

380.

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430.

320.

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133.

942.

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25C

AN

GU

RO

KA

NG

AR

OO

3.73

1.65

1.92

1.07

1.31

3.87

4.78

4.30

2.88

0.00

0.00

0.00

0.00

0.05

0.95

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0.21

0.48

BO

LL/T

OR

EK

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ILE

3.27

3.12

3.80

3.60

4.80

5.87

5.97

5.35

3.31

0.62

1.29

1.66

1.01

0.18

0.70

0.60

0.48

0.70

CO

LTE

LLO

KN

IFE

6.87

4.72

4.45

4.97

4.69

2.33

3.17

3.18

1.65

0.00

0.16

0.60

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0.13

2.40

1.89

1.52

1.68

LAM

PA

DA

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P5.

474.

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440.

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F6.

273.

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961.

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MO

NE

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5.07

4.10

3.25

3.63

3.71

2.73

3.33

3.60

1.88

0.00

0.00

0.00

0.00

0.00

2.10

1.28

0.78

1.08

LEO

NE

LIO

N4.

331.

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532.

403.

583.

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611.

261.

281.

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OP

OM

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SE

5.53

2.22

2.45

2.27

2.86

2.60

3.10

3.35

1.62

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62.

591

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56F

UN

GO

MU

SH

RO

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6.07

2.95

2.88

2.90

1.53

2.93

4.10

4.45

2.35

0.00

0.00

0.00

0.00

0.18

2.02

0.48

0.87

n.a.

CIP

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AO

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N6.

133.

423.

322.

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580.

330.

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674.

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TR

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3.27

1.72

1.52

1.10

1.41

4.87

5.97

5.55

2.88

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FO

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TA

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TS

6.80

4.75

4.55

4.87

4.88

2.27

2.92

2.83

1.54

0.22

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0.53

0.15

0.21

2.39

1.00

1.47

1.79

PE

SC

AP

EA

CH

6.20

3.80

2.90

2.87

3.65

2.40

3.45

3.74

2.12

0.27

1.12

1.19

1.12

0.68

2.12

0.60

1.12

n.a.

PA

VO

NE

PE

AC

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131.

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602.

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701.

371.

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380.

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PE

PE

RO

NE

PE

PP

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6.40

3.42

2.92

2.47

3.06

4.33

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4.78

3.23

0.00

0.22

1.07

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0.14

1.56

1.15

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402.

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MA

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5.40

2.27

2.18

1.83

2.27

2.53

3.38

3.15

1.76

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0.60

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PU

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5.73

2.47

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1.90

2.26

4.80

4.70

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2.77

0.22

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0.25

1.41

0.48

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CO

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132.

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CE

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2.67

1.80

1.52

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4.73

4.60

5.18

5.15

2.96

0.27

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0.56

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0.20

1.77

0.60

0.32

0.48

GA

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402.

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291.

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OR

BIC

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CIS

SO

RS

5.93

4.10

3.98

4.07

1.43

3.00

3.88

3.79

2.00

0.00

0.70

0.16

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0.12

1.53

0.30

0.92

1.00

PE

CO

RA

SH

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P5.

332.

351.

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832.

282.

803.

383.

601.

650.

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630.

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301.

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381.

371.

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AM

ICIA

SH

IRT

6.13

4.50

4.56

4.37

4.69

2.93

3.72

3.00

2.04

0.00

0.77

0.00

0.00

0.07

2.62

1.45

1.68

2.09

SC

AR

PA

SH

OE

5.60

4.65

4.62

4.93

4.75

2.07

2.88

2.72

1.38

0.00

0.16

0.28

0.00

0.00

1.97

1.18

1.18

1.53

GO

NN

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603.

643.

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650.

000.

320.

160.

000.

052.

171.

341.

361.

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LiT

IAS

LED

3.27

1.70

2.80

1.90

3.75

3.27

4.93

4.68

2.81

0.00

1.09

0.00

0.26

0.59

1.51

0.00

0.13

n.a.

CA

LZ

INO

SO

CK

5.93

4.30

4.52

4.97

4.51

2.87

3.05

2.44

1.58

0.00

0.83

0.00

0.00

0.00

0.85

0.70

0.95

0.85

CU

CC

HIA

JOS

PO

ON

6.80

4.72

4.50

4.93

4.67

2.00

2.72

2.45

1.35

0.00

0.57

0.16

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0.16

2.02

0.85

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SC

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TIO

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SQ

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771.

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GA

BE

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OS

TO

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4.67

3.52

3.08

3.80

3.25

3.13

4.72

4.26

2.20

0.43

0.19

0.16

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1.04

1.65

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0.93

1.23

FR

AG

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TR

AW

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RR

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971.

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270.

480.

640.

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331.

710.

600.

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AG

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320.

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412.

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RA

VA

TIA

TIE

3.27

2.95

3.80

3.33

2.55

3.93

4.85

4.42

2.38

0.00

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0.89

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2.20

1.38

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1.72

TIG

RE

TIG

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4.07

2.07

2.10

1.30

3.24

3.00

4.32

3.95

2.31

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0.16

0.33

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0.40

2.17

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PO

MO

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RO

TO

MA

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6.67

3.80

3.78

4.27

4.04

2.67

2.80

3.47

1.65

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0.00

0.80

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0.04

2.25

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TR

EN

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534.

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AM

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TR

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202.

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433.

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221.

110.

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291.

761.

351.

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RO

MB

AT

RU

MP

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4.47

2.30

2.60

1.90

1.53

3.40

4.97

5.39

2.35

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OM

BR

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LO

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BR

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6.07

3.80

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3.43

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1.56

VIO

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885.

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330.

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512.

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081.

171.

26A

NG

UR

IAW

AT

ER

ME

LON

5.93

3.77

3.05

2.53

2.00

3.20

4.47

4.08

2.81

0.27

2.12

0.55

0.47

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0.30

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0.48

PO

ZZ

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3.13

1.97

1.45

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n.a,

3.87

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2.18

2.95

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n.a.

MU

LIN

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MIL

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071.

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571.

653.

875.

57n.

a.2.

310.

000.

880.

160.

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092.

100.

301.

281.

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EB

RA

ZE

BR

A3.

801.

921.

601.

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a.3.

604.

55n.

a.2.

460.

220.

000.

000.

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a.1.

200.

300.

42n.

a.

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15,

1999

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2000

.)

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C/J

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