{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00243737,0.003750073,0.00274077,0.003730977,0.001711436,0.003601958,0.005722691,0.002764755,0.09890939],"category_scores_gemma":[0.01620137,0.0009489002,0.004674857,0.005557984,0.0007675575,0.003051918,0.003238239,0.003002768,0.1328909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001829271,"about_ca_system_score_gemma":0.004375782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01956959,"about_ca_topic_score_gemma":0.0450567,"domain_scores_codex":[0.9969319,0.0006227294,0.0003371242,0.001132068,0.000560549,0.0004155558],"domain_scores_gemma":[0.9963188,0.00114594,0.0002180138,0.00137622,0.0007169581,0.0002239753],"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":[0.0002167004,0.00004673656,0.001037721,0.001037289,0.00009252312,0.00003809626,0.00002062592,0.0007642113,0.00009274035,0.0008701978,0.9862328,0.009550355],"study_design_scores_gemma":[0.0008665547,0.00008198431,0.002664751,0.0007054503,0.0001481204,0.0002798161,0.0001096206,0.004155296,0.0007140505,0.00928755,0.9809264,0.00006037412],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005147806,0.0005147487,0.0006970089,0.0002143895,0.0001282281,0.00008339092,0.9932032,0.002225621,0.002418651],"genre_scores_gemma":[0.001097086,0.0002099687,0.001684833,0.0001947575,0.00002414231,0.0002408311,0.9950147,0.0002169346,0.001316804],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09890939,"threshold_uncertainty_score":0.3308849,"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."}}