{"id":"W4253950338","doi":"10.1515/iupac.76.0225","title":"Epithelium","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Relation (database); Hazard; Computer science; Toxicology; Medicine; Chemistry; Pharmacology; Data mining; Biology; Linguistics; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001240377,0.001704043,0.001094754,0.003379987,0.001084481,0.002982107,0.002330479,0.001500443,0.1089651],"category_scores_gemma":[0.006714382,0.0005169748,0.001498047,0.004789222,0.0004257656,0.002597214,0.002410658,0.001612051,0.1563111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001508558,"about_ca_system_score_gemma":0.002218616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01116053,"about_ca_topic_score_gemma":0.02393105,"domain_scores_codex":[0.9976677,0.0003405744,0.0003691467,0.0008277145,0.0005721007,0.0002227791],"domain_scores_gemma":[0.9974352,0.0005419749,0.0002327778,0.0008156786,0.0007688738,0.0002056154],"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":[0.00009635709,0.00003475713,0.001488676,0.0006692482,0.00002351772,0.00003941659,0.00004810708,0.0002199561,0.0002657684,0.001363836,0.9835827,0.01216778],"study_design_scores_gemma":[0.00005132929,0.00001409292,0.002418461,0.0002222139,0.0000150559,0.00009097577,0.00009576131,0.0002886096,0.0003375596,0.001301658,0.995147,0.00001730791],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000450261,0.0002206614,0.0004391708,0.0001775556,0.00008312054,0.00005638299,0.9927757,0.0008978355,0.004899307],"genre_scores_gemma":[0.0005469737,0.0001068904,0.0007665496,0.0001517425,0.00001112787,0.0001273702,0.9958883,0.0001101728,0.002290944],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8910349,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01292086018994104,"score_gpt":0.3942165288059022,"score_spread":0.3812956686159612,"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."}}