{"id":"W4242718852","doi":"10.1515/iupac.88.0749","title":"Epidemiology","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Effects and risks of endocrine disrupting chemicals","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Data mining; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002678795,0.001097985,0.001673602,0.005549375,0.0006829951,0.00231759,0.002060727,0.001474556,0.1033652],"category_scores_gemma":[0.02529561,0.0006312064,0.002222379,0.010845,0.000339387,0.00228289,0.002198042,0.001993987,0.04459914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002125426,"about_ca_system_score_gemma":0.005028636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03253078,"about_ca_topic_score_gemma":0.04188814,"domain_scores_codex":[0.9952115,0.0009209166,0.001753952,0.001059134,0.0007146876,0.0003397628],"domain_scores_gemma":[0.9878834,0.003466039,0.002282058,0.001912546,0.004017594,0.0004384566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001836684,0.00003035467,0.009426331,0.008081975,0.0002533935,0.00005970242,0.0001085858,0.0003019318,0.00009296878,0.003074334,0.9497737,0.02861314],"study_design_scores_gemma":[0.0001810774,0.00002424321,0.01663768,0.004658564,0.0001578934,0.0001804679,0.0001660468,0.0001309159,0.0001080562,0.002635943,0.9750783,0.0000406418],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003278442,0.001254192,0.0004895196,0.0003542249,0.0001366797,0.0001553638,0.9910227,0.0001302466,0.00612921],"genre_scores_gemma":[0.002767155,0.002897971,0.002190013,0.0007878318,0.0001180227,0.0008806963,0.985792,0.0001118383,0.004454549],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1033652,"threshold_uncertainty_score":0.3457911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02477779743542533,"score_gpt":0.5046432626966745,"score_spread":0.4798654652612491,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}