{"id":"W4243887488","doi":"10.1515/iupac.79.2061","title":"Subchronic","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","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; Organic chemistry","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":[0.001411673,0.001839225,0.001609969,0.004334503,0.001090107,0.003311905,0.002719456,0.001667878,0.1491689],"category_scores_gemma":[0.01114715,0.000612032,0.002220859,0.007306214,0.0004274268,0.002112709,0.002241642,0.001859715,0.1757588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00168773,"about_ca_system_score_gemma":0.003374223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02407791,"about_ca_topic_score_gemma":0.04913539,"domain_scores_codex":[0.9975805,0.000377942,0.000394457,0.0008271549,0.0004884473,0.0003314573],"domain_scores_gemma":[0.9949458,0.001151531,0.0005594261,0.001368433,0.001636315,0.0003385105],"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":[0.0001446682,0.00002021023,0.001963451,0.0009869268,0.00005690603,0.00002515873,0.00002836165,0.0001642465,0.000146624,0.0007779749,0.9905139,0.005171625],"study_design_scores_gemma":[0.0001810296,0.00002315666,0.005757926,0.0005315149,0.00006063519,0.00008724591,0.0001183424,0.0002285408,0.0002771839,0.001257226,0.991448,0.00002920881],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001602364,0.0001017762,0.000083843,0.0000552552,0.00003558821,0.00002237549,0.9980791,0.0001544045,0.001307386],"genre_scores_gemma":[0.0004146471,0.00007408671,0.0002352415,0.0001002251,0.0000120162,0.0001026655,0.9975916,0.00005532558,0.001414227],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1491689,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01560046632820747,"score_gpt":0.4235341206645273,"score_spread":0.4079336543363198,"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."}}