{"id":"W4256412328","doi":"10.1515/iupac.79.0829","title":"Anticoagulant","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Chemistry; Philosophy; Biology; Linguistics","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":[0.00146017,0.001813342,0.001420681,0.003164117,0.000919131,0.003208858,0.002645401,0.001780927,0.1860593],"category_scores_gemma":[0.01200966,0.0005178094,0.001711776,0.004651623,0.0003477696,0.002226043,0.001867196,0.001692219,0.2334503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001490345,"about_ca_system_score_gemma":0.002888168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01404045,"about_ca_topic_score_gemma":0.02625434,"domain_scores_codex":[0.9978687,0.0003347377,0.0003252183,0.0008156638,0.0004332196,0.0002224297],"domain_scores_gemma":[0.995536,0.001111142,0.0003985713,0.001203233,0.001429978,0.0003212391],"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":[0.0001243263,0.00002132961,0.001314176,0.0006636719,0.00003329918,0.00001850496,0.00001462177,0.0001364033,0.00006794205,0.0005556904,0.9896935,0.007356544],"study_design_scores_gemma":[0.000187355,0.00002215853,0.00332587,0.0004589763,0.00003785666,0.00007273891,0.00006718016,0.0002555682,0.0002120509,0.001703831,0.9936317,0.00002474934],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001314293,0.0001172953,0.0001234658,0.0001072354,0.00005617894,0.00003218258,0.9970943,0.0003532325,0.00198462],"genre_scores_gemma":[0.0004038589,0.0000908709,0.0003848193,0.0001602056,0.00001655367,0.0001312256,0.99706,0.00007286455,0.001679527],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1860593,"threshold_uncertainty_score":0.6224303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01546221680889258,"score_gpt":0.3936935486580531,"score_spread":0.3782313318491605,"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."}}