{"id":"W4236707974","doi":"10.1515/iupac.79.1986","title":"Saturable Elimination","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Hazard; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006002147,0.0003542427,0.0003903184,0.0002170603,0.0001426038,0.0002547011,0.001515325,0.0003530183,0.000292029],"category_scores_gemma":[0.0002589338,0.0002680665,0.0001159706,0.0002716726,0.00006478441,0.0003551198,0.0003353112,0.0004441143,0.000009580137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002724227,"about_ca_system_score_gemma":0.0007897139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008785027,"about_ca_topic_score_gemma":0.0001323949,"domain_scores_codex":[0.9973746,0.00008803925,0.0003829979,0.0006659775,0.001053315,0.0004350728],"domain_scores_gemma":[0.9976335,0.00008736602,0.0002305714,0.001256704,0.0006295617,0.0001623097],"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":[0.000008734078,0.00006468478,3.055863e-7,0.00003921283,0.00001834875,0.00002244135,0.000008541679,0.000005951214,0.000007519077,0.0009945983,0.9576027,0.04122697],"study_design_scores_gemma":[0.0002888861,0.0001316408,0.000006363767,0.0002921131,0.00002113548,0.00001703786,0.000002465678,0.001291844,0.000034295,0.002598636,0.9949325,0.0003831299],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000003420381,0.0003765239,0.2840536,0.001392283,0.0007631828,0.0000918419,0.7131386,0.0001319114,0.00004864768],"genre_scores_gemma":[0.00007507818,0.0006978072,0.002095544,0.0004269631,0.0004739932,0.00001119022,0.9957325,0.00001666309,0.0004702111],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2825939,"threshold_uncertainty_score":0.9999772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01835084095478086,"score_gpt":0.3834142185616266,"score_spread":0.3650633776068457,"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."}}