{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":14,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":14,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"65257bddf0fc","filters":{"venue":"Computational Toxicology"}},"results":[{"id":"W2612334248","doi":"10.1016/j.comtox.2017.05.001","title":"Performance of machine learning algorithms for qualitative and quantitative prediction drug blockade of hERG1 channel","year":2017,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; University of Alberta","funders":"National Heart, Lung, and Blood Institute; Alberta Innovates; Heart and Stroke Foundation of Canada; National Institutes of Health; University of Alberta; Li Ka Shing Foundation","keywords":"Computer science; Machine learning; Artificial intelligence; Algorithm; Qualitative analysis; Qualitative research","authors":[{"name":"Sören Wacker","is_ca":true},{"name":"Sergei Y. Noskov","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07615681208883787,"gpt":0.3929067938441664,"spread":0.3167499817553285,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004013702,0.0007005322,0.0007700763,0.0008760673,0.0003737762,0.001326543,0.0008307335,0.00125656,0.001484033],"category_scores_gemma":[0.01264788,0.0002104825,0.0004551358,0.0005585736,0.0004777701,0.001069055,0.0005222104,0.0009908307,0.0002789789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0012666,"about_ca_system_score_gemma":0.001620775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005404552,"about_ca_topic_score_gemma":0.002880184,"domain_scores_codex":[0.9991289,0.0004391728,0.00007237398,0.0001219323,0.0001731788,0.00006441822],"domain_scores_gemma":[0.9844306,0.01353835,0.0004678112,0.0004661093,0.0009469654,0.0001501941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006733973,0.0002135479,0.004567744,0.000123376,0.0001107276,0.00002668159,0.0000274319,0.9040759,0.001571677,0.003016559,0.001379929,0.08421303],"study_design_scores_gemma":[0.00001015874,0.00003284651,0.0002059779,0.000002635778,0.000004700773,0.00000538161,0.000003563617,0.9980494,0.0008246732,0.0008075071,0.00005058726,0.000002644238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7466763,0.003030683,0.236711,0.001827081,0.0002300718,0.0001103528,0.0008690078,0.003281572,0.007263893],"genre_scores_gemma":[0.9590924,0.0002573375,0.03881909,0.0001549543,0.0000389414,0.00004550757,0.0005637113,0.00007271132,0.0009552985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005404552,"threshold_uncertainty_score":0.02122676,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3207184553","doi":"10.1016/j.comtox.2021.100195","title":"Towards a qAOP framework for predictive toxicology - Linking data to decisions","year":2021,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Joint Research Centre; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; FP7 People: Marie-Curie Actions; Universiteit Leiden; Lorentz Center; European Commission; Horizon 2020 Framework Programme; European Chemical Industry Council","keywords":"Adverse Outcome Pathway; Construct (python library); Computer science; Outcome (game theory); Interpretation (philosophy); Data science; Computational biology; Biology; Programming language; Mathematics","authors":[{"name":"Alicia Paini","is_ca":false},{"name":"Ivana Campia","is_ca":false},{"name":"M Cronin","is_ca":false},{"name":"David Asturiol","is_ca":false},{"name":"Lidia Ceriani","is_ca":false},{"name":"Thomas E. Exner","is_ca":false},{"name":"Wang Gao","is_ca":false},{"name":"Caroline Gomes","is_ca":false},{"name":"Johannes W. Kruisselbrink","is_ca":false},{"name":"Marvin Martens","is_ca":false},{"name":"M.E. Meek","is_ca":true},{"name":"David Pamies","is_ca":false},{"name":"Julia Pletz","is_ca":false},{"name":"Stefan Scholz","is_ca":false},{"name":"Andreas Schüttler","is_ca":false},{"name":"Nicoleta Sp̂înu","is_ca":false},{"name":"Daniel L. Villeneuve","is_ca":false},{"name":"Clemens Wittwehr","is_ca":false},{"name":"Andrew Worth","is_ca":false},{"name":"Mirjam Luijten","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.158383014183534,"gpt":0.4262716589837185,"spread":0.2678886448001846,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01675698,0.001664493,0.001538385,0.003498358,0.001358215,0.00818762,0.004897407,0.002599921,0.008020757],"category_scores_gemma":[0.02980047,0.00105614,0.00287303,0.00393423,0.002832915,0.005271088,0.00658628,0.004031505,0.001858989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003354043,"about_ca_system_score_gemma":0.008401517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01177256,"about_ca_topic_score_gemma":0.01161914,"domain_scores_codex":[0.9927267,0.004611012,0.0006067923,0.0006061226,0.001152949,0.0002965622],"domain_scores_gemma":[0.9812421,0.01313109,0.0008493896,0.001544964,0.002762475,0.0004701363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004247177,0.00007791962,0.0009306102,0.0005066559,0.00009856278,0.0002167553,0.0004725219,0.3153296,0.0003271837,0.6176685,0.0064588,0.05787041],"study_design_scores_gemma":[0.00001700908,0.0000244714,0.00011313,0.000243841,0.00002534717,0.00004937112,0.0001626129,0.4890479,0.000369747,0.4916524,0.01827144,0.00002275986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009948992,0.000228757,0.9921824,0.001793039,0.00003444524,0.0001766559,0.0004690802,0.0003954716,0.003725225],"genre_scores_gemma":[0.05554383,0.0007316359,0.9394209,0.0004833499,0.00009071427,0.0008185311,0.001490496,0.000167466,0.001253108],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01675698,"threshold_uncertainty_score":0.08862042,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3203933639","doi":"10.1016/j.comtox.2021.100191","title":"In silico approaches in carcinogenicity hazard assessment: Current status and future needs","year":2021,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Carcinogens and Genotoxicity Assessment","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Government of Canada; Health Canada","funders":"National Institute of Environmental Health Sciences; National Institutes of Health","keywords":"In silico; Scope (computer science); Protocol (science); Computer science; Hazard; Risk analysis (engineering); Computational biology; Biology; Business; Medicine","authors":[{"name":"Raymond R. Tice","is_ca":false},{"name":"Arianna Bassan","is_ca":false},{"name":"Alexander Amberg","is_ca":false},{"name":"Lennart T. Anger","is_ca":false},{"name":"Marc A. Beal","is_ca":true},{"name":"Phillip Bellion","is_ca":false},{"name":"Romualdo Benigni","is_ca":false},{"name":"J. M. Birmingham","is_ca":false},{"name":"Alessandro Brigo","is_ca":false},{"name":"Frank Bringezu","is_ca":false},{"name":"Lidia Ceriani","is_ca":false},{"name":"Ian Crooks","is_ca":false},{"name":"Kevin P. Cross","is_ca":false},{"name":"Rosalie K. Elespuru","is_ca":false},{"name":"David Faulkner","is_ca":false},{"name":"Marie Fortin","is_ca":false},{"name":"Paul Fowler","is_ca":false},{"name":"Markus Frericks","is_ca":false},{"name":"Helga H.J. Gerets","is_ca":false},{"name":"Gloria D. Jahnke","is_ca":false},{"name":"David Jones","is_ca":false},{"name":"Naomi L. Kruhlak","is_ca":false},{"name":"Elena Lo Piparo","is_ca":false},{"name":"Juan Luis López-Belmonte","is_ca":false},{"name":"Amarjit Luniwal","is_ca":false},{"name":"Alice Luu","is_ca":true},{"name":"Federica Madia","is_ca":false},{"name":"Serena Manganelli","is_ca":false},{"name":"Balasubramanian Manickam","is_ca":false},{"name":"Jordi Mestres","is_ca":false},{"name":"Amy L. Mihalchik-Burhans","is_ca":false},{"name":"Louise Neilson","is_ca":false},{"name":"Arun R. Pandiri","is_ca":false},{"name":"Manuela Pavan","is_ca":false},{"name":"Cynthia V. Rider","is_ca":false},{"name":"John P. Rooney","is_ca":false},{"name":"Alejandra Trejo‐Martin","is_ca":false},{"name":"Karen H. Watanabe","is_ca":false},{"name":"Angela White","is_ca":false},{"name":"David Woolley","is_ca":false},{"name":"Glenn J. Myatt","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02769727882072802,"gpt":0.3026332889388217,"spread":0.2749360101180937,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00677463,0.001470428,0.002928085,0.001145696,0.0004208774,0.004765187,0.004662205,0.001920787,0.005574649],"category_scores_gemma":[0.01238332,0.0007079212,0.001535776,0.001162485,0.001486666,0.003413636,0.002098509,0.002660193,0.001434565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001057526,"about_ca_system_score_gemma":0.002450686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005929107,"about_ca_topic_score_gemma":0.006188584,"domain_scores_codex":[0.9983353,0.001008905,0.00007783158,0.0001710158,0.0003458197,0.00006112269],"domain_scores_gemma":[0.9841288,0.01359403,0.0003424294,0.00069998,0.0009273192,0.0003074767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003469861,0.001130994,0.01068085,0.005500334,0.00163,0.0001425618,0.0002813053,0.6278289,0.004800101,0.07112852,0.007064003,0.2694653],"study_design_scores_gemma":[0.0001107309,0.0002283384,0.001349814,0.0007409883,0.0004041407,0.0001186521,0.0003345121,0.8036752,0.002017755,0.1584018,0.03254315,0.00007491517],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.04581078,0.05188193,0.8703489,0.01160623,0.0004858338,0.000187822,0.001440092,0.002620632,0.01561779],"genre_scores_gemma":[0.3517924,0.09011433,0.5461955,0.003316211,0.000863835,0.0006858896,0.002887449,0.0006490572,0.003495467],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.00677463,"threshold_uncertainty_score":0.03582811,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3127947578","doi":"10.1016/j.comtox.2021.100159","title":"Assessment of the predictive capacity of a physiologically based kinetic model using a read-across approach","year":2021,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Pesticide Exposure and Toxicity","field":"Agricultural and Biological Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Health Canada","funders":"Joint Research Centre","keywords":"Computer science; Biochemical engineering; Workflow; In silico; Estragole; Context (archaeology); Experimental data; Data science; Machine learning; Chemistry; Mathematics; Biology; Database; Engineering","authors":[{"name":"Alicia Paini","is_ca":false},{"name":"Andrew Worth","is_ca":false},{"name":"Sunil Kulkarni","is_ca":true},{"name":"David J. Ebbrell","is_ca":false},{"name":"Judith C. Madden","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06905287062300335,"gpt":0.2986011900524222,"spread":0.2295483194294189,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003465501,0.001344524,0.001096693,0.0008534518,0.0005427493,0.002007212,0.001648532,0.00163522,0.002493108],"category_scores_gemma":[0.007827386,0.0004777858,0.001682231,0.0005655853,0.0006036101,0.001884065,0.001293106,0.001727642,0.0006087251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001453117,"about_ca_system_score_gemma":0.001933684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009559277,"about_ca_topic_score_gemma":0.003646912,"domain_scores_codex":[0.9992074,0.0002818296,0.00006754433,0.000193557,0.0001772826,0.0000724162],"domain_scores_gemma":[0.9955587,0.002700819,0.0005545805,0.0004806016,0.0005668801,0.0001383359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001014456,0.00006573985,0.00144995,0.00009647874,0.00004779708,0.00007667948,0.00005169684,0.9820709,0.003377761,0.005064935,0.0002094491,0.007387089],"study_design_scores_gemma":[0.00000558591,0.00006405851,0.0002186258,0.000008838721,0.000015113,0.00001623869,0.000016307,0.9954039,0.001719057,0.002137352,0.000383759,0.00001124788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.157953,0.000606737,0.829689,0.0007599909,0.00009426133,0.0002166338,0.001118949,0.001663183,0.007898304],"genre_scores_gemma":[0.8599538,0.0005984114,0.1333292,0.0002428262,0.00003561546,0.0003732868,0.001625797,0.0003455396,0.003495645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009559277,"threshold_uncertainty_score":0.01900727,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3096410258","doi":"10.1016/j.comtox.2020.100142","title":"Evaluation of quantitative structure property relationship algorithms for predicting plasma protein binding in humans","year":2020,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; U.S. Environmental Protection Agency","keywords":"Quantitative structure–activity relationship; Lipophilicity; Chemistry; Molecular descriptor; Polar surface area; Training set; Stereochemistry; Molecule; Organic chemistry; Artificial intelligence; Computer science","authors":[{"name":"Yejin Esther Yun","is_ca":true},{"name":"Rogelio Tornero‐Velez","is_ca":false},{"name":"S. Thomas Purucker","is_ca":false},{"name":"Daniel T. Chang","is_ca":false},{"name":"Andrea N. Edginton","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2105543463801471,"gpt":0.4011006702650156,"spread":0.1905463238848686,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00407205,0.00106291,0.0009874349,0.000973388,0.000173694,0.0006442502,0.0007331343,0.000854664,0.001526616],"category_scores_gemma":[0.007084363,0.000346347,0.0009078143,0.0005336283,0.0002143097,0.0004764627,0.0003525836,0.0008021765,0.0003505345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006417019,"about_ca_system_score_gemma":0.001125783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004926541,"about_ca_topic_score_gemma":0.001961951,"domain_scores_codex":[0.9988801,0.0005604681,0.00006886374,0.0002180213,0.0002288492,0.00004372127],"domain_scores_gemma":[0.9966239,0.00267922,0.0002054459,0.0001205225,0.0003194952,0.00005156111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00101418,0.0003431406,0.01069369,0.0001650511,0.0003753746,0.0001119734,0.00003079538,0.7701009,0.00246538,0.001751286,0.002002697,0.2109455],"study_design_scores_gemma":[0.00002648984,0.0001491034,0.0009360871,0.00000696951,0.00002752898,0.00003985568,0.000003748105,0.997587,0.0005402733,0.0004731188,0.0002050608,0.000004775984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3281378,0.007254324,0.6548252,0.001034517,0.0001270967,0.0004013628,0.00148779,0.003028299,0.003703691],"genre_scores_gemma":[0.8441484,0.001549721,0.1509243,0.0003076837,0.00005148524,0.0002536543,0.001456159,0.0001219138,0.001186653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004926541,"threshold_uncertainty_score":0.02153534,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285498054","doi":"10.1016/j.comtox.2022.100237","title":"Principles and procedures for assessment of acute toxicity incorporating in silico methods","year":2022,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Agriculture and Agri-Food Canada; Health Canada; Smiths Detection (Canada)","funders":"National Institute of Environmental Health Sciences; National Institutes of Health","keywords":"In silico; Acute toxicity; Computer science; Computational biology; Mechanism (biology); Toxicity; Biochemical engineering; Data mining; Biology; Medicine; Engineering","authors":[{"name":"Craig Zwickl","is_ca":false},{"name":"Jessica Graham","is_ca":false},{"name":"Robert A. Jolly","is_ca":false},{"name":"Arianna Bassan","is_ca":false},{"name":"Ernst Ahlberg","is_ca":false},{"name":"Alexander Amberg","is_ca":false},{"name":"Lennart T. Anger","is_ca":false},{"name":"Lisa Beilke","is_ca":false},{"name":"Phillip Bellion","is_ca":false},{"name":"Alessandro Brigo","is_ca":false},{"name":"Heather Burleigh-Flayer","is_ca":false},{"name":"M Cronin","is_ca":false},{"name":"Amy Devlin","is_ca":false},{"name":"Trevor Fish","is_ca":false},{"name":"Susanne Glowienke","is_ca":false},{"name":"Agnes L. Karmaus","is_ca":false},{"name":"Ray Kemper","is_ca":false},{"name":"Sunil Kulkarni","is_ca":true},{"name":"Elena Lo Piparo","is_ca":false},{"name":"Federica Madia","is_ca":false},{"name":"Matthew T. Martin","is_ca":false},{"name":"Melisa Masuda-Herrera","is_ca":false},{"name":"Britt L. McAtee","is_ca":false},{"name":"Jordi Mestres","is_ca":false},{"name":"Lawrence Milchak","is_ca":false},{"name":"Chandrika Moudgal","is_ca":false},{"name":"Moiz Mumtaz","is_ca":false},{"name":"Wolfgang Muster","is_ca":false},{"name":"Louise Neilson","is_ca":false},{"name":"Grace Patlewicz","is_ca":false},{"name":"Alexandre Tadeu Paulino","is_ca":true},{"name":"Alessandra Roncaglioni","is_ca":false},{"name":"Patricia Ruiz","is_ca":false},{"name":"David T. Szabo","is_ca":false},{"name":"Jean‐Pierre Valentin","is_ca":false},{"name":"Ioanna Vardakou","is_ca":false},{"name":"David Woolley","is_ca":false},{"name":"Glenn J. Myatt","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06981189652427372,"gpt":0.423781039084231,"spread":0.3539691425599573,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00296542,0.001156449,0.001141318,0.001560847,0.0006767658,0.001952111,0.003910542,0.001325504,0.003630478],"category_scores_gemma":[0.006232818,0.0007920993,0.001334769,0.0007680777,0.001637703,0.001333008,0.001755955,0.002533417,0.001663147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008295263,"about_ca_system_score_gemma":0.00158614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001927085,"about_ca_topic_score_gemma":0.001769668,"domain_scores_codex":[0.998254,0.0007112293,0.0000982292,0.0001194797,0.0007694551,0.00004765653],"domain_scores_gemma":[0.9971185,0.001459212,0.0001832967,0.0005966147,0.0005866809,0.00005569926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000038867,0.0001913457,0.0008075621,0.0003951157,0.0001278924,0.0001359957,0.0001296636,0.4876991,0.009083725,0.4082534,0.002845311,0.0902921],"study_design_scores_gemma":[0.00001690326,0.00005575393,0.0002088203,0.00005735464,0.00003512525,0.0001016909,0.00001777716,0.7894039,0.006416564,0.1964771,0.007178803,0.00003037994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007185682,0.00007393597,0.9973295,0.00007216356,0.00001628452,0.00005278632,0.00004394525,0.0001938299,0.001499016],"genre_scores_gemma":[0.06428432,0.0006031845,0.9306598,0.0001563435,0.00006118403,0.0005486866,0.0001668677,0.000236406,0.003283217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003910542,"threshold_uncertainty_score":0.01568282,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2752495226","doi":"10.1016/j.comtox.2017.09.001","title":"A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols","year":2017,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Health Canada; Oak Ridge Institute for Science and Education; U.S. Environmental Protection Agency; U.S. Department of Energy","keywords":"PubChem; Similarity (geometry); Phenols; Fingerprint (computing); Artificial intelligence; Computer science; Data mining; Machine learning; Chemistry; Organic chemistry; Image (mathematics)","authors":[{"name":"Prachi Pradeep","is_ca":false},{"name":"Kamel Mansouri","is_ca":false},{"name":"Grace Patlewicz","is_ca":false},{"name":"Richard Judson","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1560163300265032,"gpt":0.4448023959765993,"spread":0.288786065950096,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003399178,0.001045913,0.001231061,0.001091767,0.0004374914,0.0009188452,0.001557644,0.0007773182,0.001550923],"category_scores_gemma":[0.008100918,0.0003273489,0.0008518054,0.001239216,0.0003789071,0.001149989,0.0007906051,0.0007368386,0.0005047782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006822034,"about_ca_system_score_gemma":0.001445231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005483122,"about_ca_topic_score_gemma":0.009709604,"domain_scores_codex":[0.9985704,0.0005985316,0.00007640599,0.0003848521,0.0003175863,0.00005209447],"domain_scores_gemma":[0.9920534,0.005695193,0.0004105908,0.0009292095,0.000768376,0.0001432023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00538115,0.001771721,0.04161705,0.003343156,0.001574984,0.0008639125,0.0002196024,0.5813733,0.02907335,0.004187772,0.005135471,0.3254585],"study_design_scores_gemma":[0.0004175916,0.003953391,0.005343429,0.0001223372,0.0007646123,0.0003879322,0.0001892429,0.9322289,0.04591255,0.003019507,0.007580668,0.00007983012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9122738,0.008212699,0.06288091,0.0002606023,0.00008247084,0.0004154668,0.006125738,0.00400036,0.005747912],"genre_scores_gemma":[0.8926982,0.001753123,0.09646142,0.0001338862,0.00002964815,0.00009109337,0.007176006,0.0002451701,0.001411372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005483122,"threshold_uncertainty_score":0.01797676,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3097309053","doi":"10.1016/j.comtox.2020.100141","title":"Investigation of C-glycosylated apigenin and luteolin derivatives’ effects on protein tyrosine phosphatase 1B inhibition with molecular and cellular approaches","year":2020,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Protein Tyrosine Phosphatases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; University of Alberta; Concordia University","funders":"","keywords":"Vitexin; Luteolin; Orientin; Apigenin; Isovitexin; Chemistry; Biochemistry; Non-competitive inhibition; Isoorientin; Flavones; Flavonoid; Enzyme","authors":[{"name":"Md Yousof Ali","is_ca":true},{"name":"Susoma Jannat","is_ca":true},{"name":"Mizanur Rahman","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0190144247075248,"gpt":0.212384632447374,"spread":0.1933702077398492,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009495147,0.0002049479,0.0001963279,0.000114727,0.0001205929,0.0002340664,0.0002998095,0.0001736141,0.001301101],"category_scores_gemma":[0.0001313949,0.0000800932,0.0001983952,0.0001602346,0.0001866366,0.0001865828,0.0001111407,0.0005219688,0.0001441956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002880163,"about_ca_system_score_gemma":0.0002151302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009352344,"about_ca_topic_score_gemma":0.0009136473,"domain_scores_codex":[0.9999461,0.000006162342,0.000002632414,0.0000136031,0.00001934509,0.00001212887],"domain_scores_gemma":[0.9999615,0.00001296615,0.000009521204,0.000004714282,0.000005901592,0.000005282616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001869194,0.00005890426,0.0001272566,0.00006273077,0.00001221186,0.00005620898,0.00001095141,0.001804675,0.9938869,0.00104979,0.00005632371,0.002687059],"study_design_scores_gemma":[0.00001297231,0.0001892742,0.0006770525,0.000001981661,0.00001492938,0.00004529034,0.00001409741,0.01002072,0.9874205,0.00013577,0.001462316,0.000005101785],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890643,0.0006869968,0.006262182,0.00008393342,0.00002447008,0.00001916704,0.0002372927,0.0001172298,0.003504487],"genre_scores_gemma":[0.9964998,0.0004846416,0.001797447,0.00001676031,0.000002409325,0.00001289263,0.0001190958,0.00001426053,0.001052709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001301101,"threshold_uncertainty_score":0.004352629,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4323928799","doi":"10.1016/j.comtox.2023.100265","title":"2D-QSAR study and design of novel pyrazole derivatives as an anticancer lead compound against A-549, MCF-7, HeLa, HepG-2, PaCa-2, DLD-1","year":2023,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Université de Montréal; Hôpital Maisonneuve-Rosemont","funders":"","keywords":"Quantitative structure–activity relationship; HeLa; Pyrazole; Cancer cell lines; Chemistry; Molecular descriptor; Principal component analysis; Combinatorial chemistry; Computational biology; Stereochemistry; Cancer; Computer science; Artificial intelligence; Cancer cell; Biology; Biochemistry; In vitro","authors":[{"name":"Fatima Ezzahra Bennani","is_ca":true},{"name":"Latifa Doudach","is_ca":false},{"name":"Khalid Karrouchi","is_ca":false},{"name":"Youssef El Rhayam","is_ca":false},{"name":"Christopher E. Rudd","is_ca":true},{"name":"M’hammed Ansar","is_ca":false},{"name":"My El Abbés Faouzi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09674628269606358,"gpt":0.3813448281958871,"spread":0.2845985454998234,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002399141,0.0003667145,0.0008536052,0.0003230069,0.000190419,0.0003305552,0.0004047002,0.0002447291,0.001635608],"category_scores_gemma":[0.000277509,0.000174853,0.0007494554,0.0004824038,0.0001118323,0.0001777477,0.0001635592,0.0003877026,0.0001799286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000353592,"about_ca_system_score_gemma":0.0005429674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003999894,"about_ca_topic_score_gemma":0.005344782,"domain_scores_codex":[0.999931,0.00002127829,0.000004030759,0.00001056211,0.00001968121,0.00001350199],"domain_scores_gemma":[0.9999307,0.00003345341,0.00001174019,0.000003868945,0.00001441707,0.000005784601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001003745,0.001199955,0.00350906,0.001108826,0.0002909955,0.0004176147,0.00008519314,0.6845241,0.2618729,0.003171232,0.001794189,0.04102223],"study_design_scores_gemma":[0.0002016244,0.00186304,0.002981269,0.0000201592,0.0003029767,0.00009931935,0.0000457279,0.9187381,0.07304174,0.000235983,0.002444744,0.00002534288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716584,0.004481058,0.01754776,0.0003454162,0.00003498807,0.0001229395,0.001202097,0.0002602254,0.004347117],"genre_scores_gemma":[0.9868967,0.001793028,0.008954682,0.00006626645,0.000007572352,0.00006178255,0.0007474321,0.00001386677,0.001458701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003999894,"threshold_uncertainty_score":0.007953227,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4327976397","doi":"10.1016/j.comtox.2023.100263","title":"Efficient large-scale mechanism-based computation of skin permeability","year":2023,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Advancements in Transdermal Drug Delivery","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Permeation; Biological system; Permeability (electromagnetism); Applicability domain; In silico; Discretization; Computer science; Multiphysics; Physiologically based pharmacokinetic modelling; Chemistry; Relative permeability; Biomedical engineering; Quantitative structure–activity relationship; Mathematics; Pharmacokinetics; Physics; Machine learning; Thermodynamics; Engineering; Bioinformatics; Porosity; Finite element method; Mathematical analysis","authors":[{"name":"Abdullah Hamadeh","is_ca":true},{"name":"Andrea N. Edginton","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08053990709815294,"gpt":0.4265942138391486,"spread":0.3460543067409956,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003832775,0.0007194634,0.001371956,0.0005572936,0.000806311,0.001324491,0.001952917,0.002173973,0.005341381],"category_scores_gemma":[0.002659457,0.0007410168,0.0008124664,0.0005918127,0.0009021947,0.001456711,0.001310717,0.001380056,0.0007146172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009769854,"about_ca_system_score_gemma":0.001457057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008466545,"about_ca_topic_score_gemma":0.00738568,"domain_scores_codex":[0.9998361,0.0000400404,0.000006265874,0.00002683866,0.00005575597,0.00003495067],"domain_scores_gemma":[0.9989727,0.0006402936,0.00006602717,0.000111049,0.0001259159,0.00008419013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004789396,0.00005345282,0.0003841555,0.00005809369,0.00002484969,0.00008306844,0.00003823468,0.985742,0.001439193,0.007367455,0.000877806,0.003883929],"study_design_scores_gemma":[0.00000773315,0.000002913715,0.00003427802,0.000001199751,0.000001547172,0.000004638897,0.000004651633,0.9980951,0.0001279336,0.001620277,0.00009785697,0.000001909249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3326265,0.001281037,0.6067588,0.002292933,0.0004092578,0.0002439445,0.00137006,0.003606217,0.05141125],"genre_scores_gemma":[0.9515702,0.0002024908,0.04378762,0.0002413031,0.0000569606,0.0001487299,0.0002902757,0.000310082,0.003392303],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008466545,"threshold_uncertainty_score":0.0178687,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413363812","doi":"10.1016/j.comtox.2025.100374","title":"Conservative consensus QSAR approach for the prediction of rat acute oral toxicity","year":2025,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Quantitative structure–activity relationship; Toxicity; Acute toxicity; Computer science; Artificial intelligence; Machine learning; Computational biology; Pharmacology; Medicine; Biology; Internal medicine","authors":[{"name":"Jerry Achar","is_ca":true},{"name":"James W. Firman","is_ca":false},{"name":"M Cronin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05445501392409084,"gpt":0.3457392722519085,"spread":0.2912842583278177,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002666035,0.0009909888,0.001201163,0.001129486,0.0003385413,0.0008138077,0.001214493,0.0006347637,0.001345099],"category_scores_gemma":[0.00518927,0.0003201731,0.001095548,0.0007534515,0.0003432395,0.0006863035,0.0008009505,0.001194791,0.0002969659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006720293,"about_ca_system_score_gemma":0.001494879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004739432,"about_ca_topic_score_gemma":0.004827423,"domain_scores_codex":[0.9990507,0.0004477839,0.00005269659,0.0001788888,0.0002088333,0.0000612838],"domain_scores_gemma":[0.9977617,0.001547925,0.0002208553,0.000128036,0.0002772369,0.00006422109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002102664,0.00004880612,0.003009246,0.0001325521,0.0001598249,0.00005962641,0.00003401301,0.9729288,0.003213897,0.001651853,0.0005353504,0.01801574],"study_design_scores_gemma":[0.00002126751,0.0001712452,0.0007373508,0.0000107005,0.00003770362,0.00002243095,0.00001846813,0.9945983,0.001349643,0.002612153,0.0004088088,0.00001191622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4920566,0.002204974,0.4954468,0.0006462795,0.000087032,0.0001867075,0.002247636,0.001543361,0.00558056],"genre_scores_gemma":[0.9586636,0.0003149331,0.03838924,0.0001475875,0.00001801488,0.0001504033,0.001438151,0.00005882371,0.0008191505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004739432,"threshold_uncertainty_score":0.01409954,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3021621917","doi":"10.1016/j.comtox.2020.100124","title":"Human exposure to synthetic endocrine disrupting chemicals (S-EDCs) is generally negligible as compared to natural compounds with higher or comparable endocrine activity. How to evaluate the risk of the S-EDCs?","year":2020,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Effects and risks of endocrine disrupting chemicals","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Endocrine system; Hormone; Endogeny; Receptor; Chemistry; Biological activity; Pharmacology; Endocrinology; Biology; In vitro; Biochemistry","authors":[{"name":"Herman Autrup","is_ca":false},{"name":"Frank A. Barile","is_ca":false},{"name":"Colin Berry","is_ca":false},{"name":"Bas J. Blaauboer","is_ca":false},{"name":"Alan R. Boobis","is_ca":false},{"name":"Herrmann Bolt","is_ca":false},{"name":"Christopher J. Borgert","is_ca":false},{"name":"W. Dekant","is_ca":false},{"name":"Daniel R. Dietrich","is_ca":false},{"name":"José L. Domingo","is_ca":false},{"name":"Gio Batta Gori","is_ca":false},{"name":"Helmut Greim","is_ca":false},{"name":"Jan G. Hengstler","is_ca":false},{"name":"Sam Kacew","is_ca":true},{"name":"H Marquardt","is_ca":false},{"name":"Olavi Pelkonen","is_ca":false},{"name":"Kai Savolainen","is_ca":false},{"name":"J. S. Heslop‐Harrison","is_ca":false},{"name":"Nico Vermeulen","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02367756510287285,"gpt":0.3389476953841292,"spread":0.3152701302812563,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008261942,0.000544814,0.0008237134,0.0004908249,0.0002113878,0.001563911,0.001113127,0.0008335324,0.003432989],"category_scores_gemma":[0.005157494,0.0002138107,0.0007165973,0.0006623691,0.0007510966,0.002212471,0.0007290345,0.0007956006,0.0006237532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006043816,"about_ca_system_score_gemma":0.001398287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005949295,"about_ca_topic_score_gemma":0.004357435,"domain_scores_codex":[0.9996268,0.0001396096,0.00001466182,0.00006715875,0.0001275802,0.00002421585],"domain_scores_gemma":[0.9983861,0.001082618,0.0001377356,0.000131796,0.0002048852,0.00005694624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001545149,0.0001195261,0.01499815,0.001952521,0.0005507517,0.0001946988,0.00007969586,0.645248,0.006771336,0.1370049,0.01536676,0.1775592],"study_design_scores_gemma":[0.00006321289,0.0002783203,0.006118127,0.0005908243,0.0002888906,0.0003011419,0.00028296,0.5660486,0.01011912,0.3675499,0.04826992,0.00008896993],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1705215,0.02220523,0.7073822,0.0220928,0.001209089,0.00009247124,0.004661143,0.001309917,0.07052568],"genre_scores_gemma":[0.8468501,0.02809371,0.1118076,0.001702563,0.0003952001,0.0001557221,0.001870025,0.0003634319,0.008761697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005949295,"threshold_uncertainty_score":0.01182932,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411659742","doi":"10.1016/j.comtox.2025.100364","title":"An in silico protocol for endocrine activity assessment: Integrating predictions, experimental evidence, and expert reviews across estrogen, androgen, thyroid, and steroidogenesis modalities","year":2025,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Effects and risks of endocrine disrupting chemicals","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Health Canada","funders":"","keywords":"In silico; Endocrine system; Androgen; Estrogen; Protocol (science); Thyroid; Modalities; Computer science; Bioinformatics; Computational biology; Medicine; Biology; Hormone; Internal medicine; Pathology; Biochemistry","authors":[{"name":"Candice Johnson","is_ca":false},{"name":"Sue Marty","is_ca":false},{"name":"Marlene T. Kim","is_ca":false},{"name":"Kevin M. Crofton","is_ca":false},{"name":"Alessandra Roncaglioni","is_ca":false},{"name":"Arianna Bassan","is_ca":false},{"name":"Tara S. Barton‐Maclaren","is_ca":false},{"name":"Ana Patrícia Domingues","is_ca":false},{"name":"Markus Frericks","is_ca":false},{"name":"Agnes L. Karmaus","is_ca":false},{"name":"Sunil Kulkarni","is_ca":true},{"name":"Elena Lo Piparo","is_ca":false},{"name":"Stephanie Melching‐Kollmuss","is_ca":false},{"name":"Ray Tice","is_ca":false},{"name":"David Woolley","is_ca":false},{"name":"Kevin P. Cross","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04251475105174269,"gpt":0.4747278853567099,"spread":0.4322131343049672,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007112763,0.001625171,0.0008992511,0.002168673,0.0008551768,0.002417135,0.002252023,0.001648344,0.03532261],"category_scores_gemma":[0.02970708,0.0008652552,0.002223845,0.001421613,0.0005162194,0.001640707,0.002431393,0.001555477,0.006695394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00118105,"about_ca_system_score_gemma":0.006670552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00504737,"about_ca_topic_score_gemma":0.01138689,"domain_scores_codex":[0.9978167,0.001106695,0.0003059793,0.0003553081,0.0003223837,0.0000929755],"domain_scores_gemma":[0.9819462,0.01390444,0.0005388608,0.001245891,0.00204509,0.0003195764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002714789,0.0009761319,0.01551641,0.009128068,0.001980337,0.002024209,0.00154003,0.4615332,0.01495914,0.06119084,0.2089778,0.2194591],"study_design_scores_gemma":[0.00126852,0.0003085581,0.002508393,0.0009197069,0.001206379,0.0004572683,0.0005335256,0.6665584,0.0184774,0.1075354,0.2000248,0.0002015815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02834452,0.0005883825,0.7990066,0.004925976,0.0006357802,0.005735634,0.1043092,0.0301288,0.02632512],"genre_scores_gemma":[0.1361955,0.0007092506,0.7796273,0.00126929,0.0001548068,0.00867821,0.0646853,0.002254793,0.006425499],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03532261,"threshold_uncertainty_score":0.1181659,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W131119879","doi":"10.1016/b978-0-12-396461-8.00005-1","title":"Modeling of Sensitive Subpopulations and Interindividual Variability in Pharmacokinetics for Health Risk Assessments","year":2013,"lang":"en","type":"book-chapter","venue":"Computational Toxicology","topic":"Effects and risks of endocrine disrupting chemicals","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Toxicodynamics; Physiologically based pharmacokinetic modelling; Monte Carlo method; Markov chain; Population; Risk assessment; Markov chain Monte Carlo; Toxicokinetics; Computer science; Risk analysis (engineering); Econometrics; Environmental health; Medicine; Biology; Statistics; Bioinformatics; Pharmacokinetics; Mathematics; Machine learning","authors":[{"name":"Kannan Krishnan","is_ca":true},{"name":"Brooks McPhail","is_ca":false},{"name":"Weihsueh A. Chiu","is_ca":false},{"name":"Paul D. White","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03278801538653149,"gpt":0.4012475158546819,"spread":0.3684595004681504,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009503414,0.0008072781,0.001079702,0.0003032462,0.0002566969,0.001040933,0.001482404,0.001141629,0.003146633],"category_scores_gemma":[0.003321304,0.0005109498,0.001084168,0.0004697588,0.0004209669,0.001178714,0.0008722362,0.001489427,0.000727541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007716921,"about_ca_system_score_gemma":0.0008364465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005960408,"about_ca_topic_score_gemma":0.004887766,"domain_scores_codex":[0.9997075,0.0001284327,0.0000132652,0.00006688198,0.00006374106,0.00002016676],"domain_scores_gemma":[0.9988691,0.0008839881,0.00006471497,0.0000753135,0.00007832096,0.0000285921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003449354,0.00004064115,0.001141755,0.00008973878,0.0001120743,0.00008220291,0.00008777509,0.9295812,0.002115701,0.03231969,0.004138502,0.03025623],"study_design_scores_gemma":[0.00000435036,0.00001044902,0.0002545119,0.00001012787,0.00001943051,0.00003984768,0.00000964518,0.960916,0.0003528449,0.03545718,0.002915803,0.000009809448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01518732,0.002038639,0.9732493,0.0009036231,0.0001050906,0.00003394841,0.0005381211,0.0003984815,0.007545468],"genre_scores_gemma":[0.5902231,0.006571497,0.3607941,0.0009806033,0.0004898023,0.0006142159,0.001474392,0.0007898102,0.03806243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005960408,"threshold_uncertainty_score":0.01185143,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}