dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors are grateful to the directors of the riding schools and their staffs for allowing us to work with their horses and for their help and understanding. The authors wish to thank all the staff of  FNARC  ( Fédération Nationale d'Amélioration de la Race Chevaline, Tunisia ) for providing the facilities to complete this study. We are grateful to  L Dupont  and  C Lavertue  for their help with data collection. We also thank  A-M Mounier  ( INRA-UMR SENAH ) and  E Klobetz-Rassam  ( University of Veterinary Medicine, Vienna ) for their technical help. The authors are grateful to  R Meagher ,  M Buob  and  L Schnablegger  (Writing and  Learning Services ,  University of Guelph ) for correcting the English of this manuscript, and to  R Meagher  and  G Mason  for helping analysing data.
In order to better understand the reasons for this, historical in vivo rabbit data were analysed in detail and resampled with the purpose of (1) revealing which of the in vivo endpoints are most important in driving United Nations Globally Harmonized System/European Union Regulation on Classification, Labelling and Packaging (UN GHS/EU CLP) classification for serious eye damage/eye irritation and (2) evaluating the method's within-test variability for proposing acceptable and justifiable target values of sensitivity and specificity for alternative methods and their combinations in testing strategies. Among the Cat 1 chemicals evaluated, 36-65 % (depending on the database).
2010). Accurate analyses of the in vivo Draize eye test data are important for several reasons. The outcome of validation studies depends not only on the performance of the in vitro method but also on the quality and variability of the in vivo data that usually serve as a reference point for comparison, in the absence of standardised human data. Chemicals that do not have reliable in vivo data of sufficient quality should not be selected as reference chemicals. It is therefore imperative that the quality of the in vivo data is carefully assessed, so that a robust selection of candidate chemicals for validation studies can be made. The impact of the variability of the in vivo Draize eye test, including inter alia the "in vivo within-test variability", on the final classification of a chemical is another important issue for the selection of reference chemicals.
The historical in vivo Draize eye test data used in this work originated from several sources including many industry sectors, and no new in vivo data were generated to perform the analyses. These data were obtained from (1) three Reference Chemicals Databases (RCD) containing data mostly on "existing chemicals" (on the EU industrial market before 1981) and (2) from the European New Chemicals Database (NCD) of the ex-European Chemicals Bureau (ex-ECB) containing data on "new chemicals" notified under Directive 67/548/EEC (introduced to the EU industrial market after September 1981). The RCD contains commercially available chemicals that have been commonly used in validation studies of in vitro methods and for which full in vivo Draize eye test data are available. The NCD, on the other hand, contains proprietary chemicals that have not been commonly used in validation studies, includes more complex chemistries and contains only summarised in vivo Draize eye test data.
Databases The in vivo rabbit eye irritation data were collected from data registered in the NCD of the ex-ECB and three Reference Chemicals Databases (RCD), i. e. (1) the Eye Irritation Reference Chemicals Data Bank (ECETOC)  (Bagley et al. 1992a (Bagley et al. , 1999a;; ECETOC 1998) , (2) the database from ZEBET  (Spielmann et al. 1996) , and (3) the database from Laboratoire National de la Santé (LNS)  (Gautheron et al. 1992). Before starting exploratory data analysis, a quality check of the Draize eye test data was performed. Studies Table  1  Draize eye studies from various data sources RCD Reference Chemicals Databases, NCD European New Chemicals Database a (i) if study criteria allowing an unambiguous classification were not met (SCNM), (ii) if less than three animals were used and no severe and/ or persistent effects were observed, (iii) in case of incomplete data or (iv) in case chemicals were classified in the absence of a classification triggering effect (i.
e. not based on a second (n = 3 or 4) or third (n = 5 or 6) highest animal mean tissue score above the classification threshold or on persistence of an effect) and the reason of classification was not indicated were excluded from the final analysis if (1) the study criteria allowing an unambiguous classification were not met (SCNM), i. e. studies of less than 21 days where full reversibility (scores equal to zero) of all endpoints in all animals did not occur and no severe effects were observed, (2) less than three animals were used and no severe and/or persistent effects were observed, (3) data were incomplete, i. e. missing data for at least one endpoint or no individual data accessible (NCD n > 3 animals), or (4) classification in the original database occurred without a tissue score above the threshold value or persistence of effects being noted and where the reason of classification was not indicated (chemicals classified in the absence of a classification triggering effect) (Table  1 ).
Draize eye test method The historical data from the RCD and NCD were generated with albino rabbits according to the OECD Test Guideline 405  (OECD 2012a). Briefly, a single dose (100 μl volume for liquids and solids) of the test material was placed into the conjunctiva cul-de-sac of the eye of the animal. Solid chemicals in the LNS database and at least the ones tested after 2002 in the NCD were rinsed from the eye 1 h after application with saline or distilled water when the solids were still present (according to the 2002 revision of OECD TG 405, even though LNS predates this revision), while solid chemicals in the ECETOC and ZEBET databases were not (these chemicals were tested according to OECD TG 405 prior to its 2002 revision). Subsequently, the eyes were observed at selected time intervals for up to 21 days for signs of ocular lesions.
Within-test variability To assess the within-test variability, the individual rabbit data were used. As mentioned above, mean CO, IR, CR, and CC values over the reading times at 24, 48, and 72 h were calculated for each animal. According to the Draize scale, the CO and CC grading can assume values of 0, 1, 2, 3, or 4. This means that for the individual rabbits, the mean over the reading times at 24, 48, and 72 h can take 13 different values (from 0 to 4 in steps of 0. 33). This rule also applies to the other endpoints except that the maximum scores for IR and CR are 2 and 3, thus resulting in seven and ten possible mean values, respectively. Sometimes, the CO, IR, CR, or CC mean scores for the animals, and as a consequence, the second (n = 3 or 4) or third (n = 5 or 6) highest mean scores (CO Maj , IR Maj , CR Maj or CC Maj ), deviated from the possible values.
Overview of the compiled data set: distribution according to UN GHS/EU CLP classification Table  1  shows the total number of in vivo Draize eye studies present in each of the databases used in the present analyses. The RCD (ECETOC, ZEBET, and LNS) contains 344 studies, of which 70 were excluded from the analyses for not fulfilling the quality criteria defined in chapter 2. 1. The NCD in turn contains 2319 individual studies, of which 459 were excluded. In total, 274 RCD studies and 1,860 NCD studies were used in the present analyses. Table 1 also presents the distribution of UN GHS/EU CLP categories in the RCD and NCD. The proportion of classified chemicals in the RCD is 39. 8 % (17. 2 % Cat 2 and 22. 6 % Cat 1), as compared to only 17. 4 % (10. 4 % Cat 2 and 6. 9 % Cat 1) in the NCD. The RCD in general also contains more Cat 1 than Cat 2 chemicals, whereas the opposite is observed for the NCD (Table  1 ).
9 and 34. 0 % of all Cat 2 chemicals in the RCD and NCD, respectively) (Table  5 ). Importantly, also a considerable amount of Cat 2 chemicals were classified based on conjunctiva effects without showing classifiable CO effects (RCD: 25. 5 %; NCD: 43. 3 %). Altogether, an impressive 89. 4 and 77. 3 % of the Cat 2 chemicals in the RCD and NCD, respectively, showed sufficient conjunctiva effects to generate a Cat 2 classification per se. Of the two conjunctiva effects, CR is the predominant one in all databases, with CC rarely appearing without parallel CR effects (<10 %). A very small proportion (2. 6 %) of the chemicals in the NCD was classified Cat 2 because of iritis only. The effects on the iris seem therefore to be of lesser importance for classification of chemicals under the UN GHS/EU CLP system.
33, 0. 33 (second highest value = 0. 33) places the chemical in the column corresponding to CO Maj equal to 0. 33, with the individual animal mean scores added in the respective rows (i. e. 2 counted in the row corresponding to 0. 33, and 1 counted in the row corresponding to 0). In total, 25 chemicals (83 animals) of Proportions that fall in the light-grey area (No Cat range) correspond to animals with mean CO < 1 that were tested with chemicals with CO Maj < 1; Proportions that fall in the grey area (Cat 2 range) correspond to animals with 1 ≤ mean CO < 3 that were tested with chemicals with 1 ≤ CO Maj < 3; Proportions that fall in the dark-grey area (Cat 1 range) correspond to animals with mean CO ≥ 3 that were tested with chemicals with CO Maj ≥ 3; Proportions in bold (diagonal) correspond to animals with mean CO scores equal to the CO Maj of the chemicals tested in those animals CO corneal opacity, NCD European New Chemicals Database, RCD Reference Chemicals Databases Proportions that fall in the light-grey area (No Cat range) correspond to animals with mean IR < 1 that were tested with chemicals with IR Maj < 1; Proportions that fall in the grey area (Cat 2 range) correspond to animals with 1 ≤ mean IR ≤ 1.
5 that were tested with chemicals with 1 ≤ IR Maj ≤ 1. 5; Proportions that fall in the dark-grey area (Cat 1 range) correspond to animals with mean IR > 1. 5 that were tested with chemicals with IR Maj > 1. 5; Proportions in bold (diagonal) correspond to animals with mean CO scores equal to the IR Maj of the chemicals tested in those animals IR iritis, NCD European New Chemicals Database, RCD Reference Chemicals Databases the RCD had CO Maj score equal to 0. 33, with individual mean CO scores ranging from 0 (22. 9 % of the animals) to 2. 67 (1. 2 % of the animals). The within-test variability for the mean CO, IR, CR, and CC scores was very similar for the RCD and NCD (Tables 6, 7, 8, 9). These figures only reflect the variability observed over the first 3 days for each tissue effect separately and do not take into account the timing of reversibility.
33 (Fig. 2b ). Of note, the mean CR scores are equal to or greater than 0. 33 but less than 2 for the majority of these animals in both databases (Fig. 2a ). In both the RCD and NCD, the majority of the animals tested with chemicals that were classified Cat 2 based on 1 ≤ CO Maj < 3 have mean CO scores between the Cat 2 and the Cat 1 classification thresholds (Fig. 1a ). However, the animal mean CO scores are generally lower in the NCD than in the RCD. The mean IR scores in the NCD and RCD, on the other hand, are less than 1 in the majority of the animals tested with this group of chemicals (Fig. 1b ). Furthermore, in both the NCD and RCD, the mean CR scores of the Cat 2 chemicals that were classified based on 1 ≤ CO Maj < 3 are equal to or greater than 2 in the majority of the animals (75.
0 % (93/124) in the RCD and 59. 4 % (187/315) in the NCD), but with a larger proportion being below this score in the NCD than in the RCD (Fig. 2a ). The same was observed for the animal mean CC scores in the RCD database. For the NCD, however, the mean CC scores are less than 2 in the majority of the animals. In the NCD and RCD, the majority of the animals tested with chemicals that were classified Cat 2 based on conjunctiva effects, but without enough corneal involvement to generate a classification, have mean CR scores that are above the Cat 2 classification threshold (Fig. 2a ) but have mean CO, IR, and CC scores that are below the Cat 2 classification threshold (Figs. 1a, 2a, b ). This means that the great majority of these chemicals were classified based on CR Maj only (see also Table  5 ).
In both the RCD and NCD, the majority of the animals tested with chemicals that were classified Cat 1 based on CO Maj ≥ 3 have mean CO scores equal to or greater than 3 (Fig. 1a ). The mean CO scores of the animals tested with chemicals that were classified Cat 1 based on a CO = 4 (and CO Maj < 3), IR Maj > 1. 5 only or Persistence only are generally above the CO Cat 2 classification threshold and mostly below the Cat 1 classification threshold (1 ≤ mean CO < 3) for both databases (Fig. 1a ). It is important to note, however, that for the NCD, about 25 % (50/198) of the animals tested with chemicals that 1 3 were classified Cat 1 based on persistence only have both mean CO and mean IR scores less than 1 (Fig. 1 ). The mean CR scores of the animals tested with classified chemicals cover in general the same range independently of the classification (Cat 2 or Cat 1) or the classification driver (Fig.
Separate resampling analyses were then performed on each of these individual data pools. The individual simulated chemicals obtained from the different resamplings were therefore built either (1) on animals coming from studies with chemicals classified with the same UN GHS/EU CLP category (i. e. No Cat, Cat 2, or Cat 1) independently of the classification driver (taking all classification drivers together) or (2) on animals coming from studies with chemicals classified with the same UN GHS/EU CLP category and having the same endpoint driving such classification. For the chemicals of the NCD, only the individual animal mean tissue scores calculated over the reading times at 24, 48, and 72 h are available. Information on persistence of effects is only available in the form of comments, often not specifying in which tissues and in how many animals the persistence of effects was observed.
Identical results were obtained for the NCD data pool. Thus, resampling from the RCD and NCD data pools of the non-classified compounds rarely resulted in misclassifications, which may be explained by the fact that the great majority of the individual animal mean CO, IR, CR, and CC scores observed for the No Cat chemicals in both databases are below the Cat 2 classification cut-offs (Figs. 1, 2 ). The total number of animals available for estimating the resampling probabilities for the UN GHS/EU CLP Cat 2 was 162 for the RCD and 582 for the NCD (Table  10 ). Only a small proportion of the simulated chemicals from the RCD was under-predicted (3. 9 %) and very few were over-predicted (0. 6 %). For the NCD, however, the underprediction rate was substantially higher (11. 7 %) but again only 0.
Resampling probabilities in function of physical state It was further explored if the physical state of the chemical had an impact on the within-test variability. Therefore, the same resampling analyses as described in the previous section were performed on the liquids and solids separately. These results are presented in Tables  11  and 12. Data on the physical state could not be retrieved for 20 chemicals from the ZEBET database (RCD) and for one chemical of the NCD, and therefore, these chemicals were not included in these analyses. The separate resampling for the liquids and solids of the NCD resulted in a slightly higher overall under-prediction of Cat 2 chemicals for the solids (12. 7 %) than for the liquids (9. 8 %). The misclassification error for the 1 ≤ CO Maj < 3 classification driver was also higher for solids (5.
Discussion For more than two decades, scientists have been trying to replace the regulatory Draize eye test by in vitro methods, but so far only partial replacement has been achieved. In order to better understand the reasons for this apparent Table  11  Resampling probabilities of 10,000 theoretical chemicals according to UN GHS/EU CLP criteria-liquids Proportions in bold represent agreement between the predicted classification and the true classification RCD Reference Chemicals Databases, NCD European New Chemicals Database, CO corneal opacity, CR conjunctiva redness, CC conjunctiva chemosis, IR iritis, NA not applicable a None of the chemicals in this group was classified based on the driver 1 ≤ IR Maj ≤ 1. 5 only b second (n = 3 or 4) or third (n = 5 or 6) highest mean score c The largest of the second (n = 3 or 4) or third (n = 5 or 6) highest mean CR and CC scores d Observed any time during the observation period (before day 21) e Sample size is too small to perform a separate resampling analysis for this driver f Individual animal data only accessible for the mean scores of days 1-3.
Furthermore, 10 % were classified as UN GHS/EU CLP Cat 2 (reversible effects on the eye/irritant to eyes) and 7 % as UN GHS/EU CLP Cat 1 (irreversible effects on the eye/serious eye damage). This prevalence is expected to be closer to the reality than what is observed in the RCD, which contains a higher number of classified chemicals, since the NCD contains all chemicals registered by multiple industry sectors since 1981, while the RCD contains a limited number of chemicals that were put together in databases mainly to support validation studies. Differences in the endpoints driving classification were also observed between the RCD and NCD. For example, 65 % of the Cat 1 chemicals in the NCD were classified based on persistence of effects only, whereas this was 35 % for the RCD. Importantly, chemicals that were classified Cat 1 based only on persistence of effects in both the RCD and NCD have individual animal mean CO scores that are mostly below 3 (in 74/76 rabbits) and mean IR scores that are mostly below 1.
5 (in 72/76 rabbits), i. e. below the CO and IR Cat 1 classification thresholds (Fig. 1 ). In fact, in the NCD, more than 25 % of the animals from this group of Cat 1 chemicals actually have mean CO scores below 1, while most of them in both the NCD and RCD have mean IR scores below 1, i. e. the CO and IR No Cat score ranges. Table  12  Resampling probabilities of 10,000 theoretical chemicals according to UN GHS/EU CLP criteria-solids Proportions in bold represent agreement between the predicted classification and the true classification RCD Reference Chemicals Databases, NCD European New Chemicals Database, CO corneal opacity, CR conjunctiva redness, CC conjunctiva chemosis, IR iritis, NA not applicable a None of the chemicals in this group was classified based on the driver 1 ≤ IR Maj ≤ 1.
The under-prediction rate for the Cat 1 subgroup that was classified based on persistence of effects only increased to 22 % when only liquids were considered. These three classification drivers largely contribute to the observed overall under-prediction probability of Cat 2 chemicals as No Cat of 4 % (RCD) and 12 % (NCD) and to an overall under-prediction probability of Cat 1 chemicals as Cat 2 of at least 11 %. A similar analysis performed on data of 723 rabbits tested in 181 studies found under-classification probabilities ranging from 4. 30 to 13. 24 %, with the estimated under-classification rate also being higher for chemicals classified based on persistence only  (Haseman et al. 2005). The under-prediction probabilities for the chemicals classified as Cat 2 based on 1 ≤ CO Maj < 3 and for the chemicals classified as Cat 1 based on CO Maj ≥ 3 were generally smaller than those obtained for the other classification drivers, ranging from 1 to 6 %, depending on the database, the classification driver and the physical state of the chemical.
Thus, if the within-and between-laboratory variability of the Draize eye test were to be added to the within-test variability reported here, it is expected that the overall variability of the in vivo method would even increase. For all the reasons above, the in vivo under-prediction rates reported in this study should be taken into account when evaluating alternative methods/strategies to replace the animal test. Lovell (1996)  performed a principal component analysis on Draize eye tissue scores obtained 24, 48, and 72 h after instillation of a test substance into the rabbit eye for fiftyfive chemicals from the ECETOC database and found that the first component, which was highly correlated with the maximum individual weighted scores (used to calculate the modified maximum average score, MMAS), could already explain 76 % of the variability.
e. severe corneal lesions, persistence of corneal effects in the absence of severe lesions, and conjunctiva effects, but also only those having high-quality in vivo data, resulting in less questionable and more reproducible classifications  (De Wever et al. 2012; Barroso et al. 2013). At the same time, the UN GHS and EU CLP decision criteria to classify chemicals based on Draize eye test data should be critically reviewed. The resampling analyses presented here show that the Draize eye test is prone to high misclassification errors. Importantly, these misclassification errors are unidirectional towards lower classifications, which demonstrates that the way the Draize eye test data are interpreted is very conservative and may over-predict the true irritation potential of chemicals; indeed, comparative studies between rabbit and man have shown that the Draize eye test is likely to overestimate the effects in man  (Roggeband et al.
Distribution of the individual tissue scores on day 21 by endpoint for the chemicals from the RCD (ECETOC and ZEBET) that resulted in a persistent effect in at least one animal (n = 124 animals) a CO corneal opacity, IR iritis, CR conjunctiva redness, CC conjunctiva chemosis, NA not applicable a Individual data for day 21 were only available for ECETOC and ZEBET databases. For 2 out of 124 animals, the study stopped before day 21 and the tissue score was not reversed to 0; these two animals were therefore not included in this table.
Within-test variability of the mean CC scores calculated over the reading times at 24, 48, and 72 h. Proportion of animals within each CC Maj group Proportions that fall in the light-grey area (No Cat range) correspond to animals with mean CC < 2 that were tested with chemicals with CC Maj < 2; Proportions that fall in the grey area (Cat 2 range) correspond to animals with mean CC ≥ 2 that were tested with chemicals with CC Maj ≥ 2; Proportions in bold (diagonal) correspond to animals with mean CC scores equal to the CC Maj of the chemicals tested in those animals CC conjunctiva chemosis, NCD European New Chemicals Database, RCD Reference Chemicals Databases 2 classification threshold for the respective tissue: 3. 5 % for CO (23/651), 4. 9 % for IR (39/790), 3. 2 % for CR (21/663) and 4. 3 % for CC (32/745) of the animals in the RCD and 2.
Resampling probabilities of 10,000 theoretical chemicals according to UN GHS/EU CLP criteria Proportions in bold represent agreement between the predicted classification and the true classification RCD Reference Chemicals Databases, NCD European New Chemicals Database, CO corneal opacity, CR conjunctiva redness, CC conjunctiva chemosis, IR iritis, NA not applicable a None of the chemicals in this group was classified based on the driver 1 ≤ IR Maj.