{"id":"W4254910917","doi":"10.1515/iupac.79.1530","title":"Lavage","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":[0.001552651,0.001791294,0.001397752,0.003475047,0.001011735,0.003727158,0.002639932,0.001802592,0.182803],"category_scores_gemma":[0.01199499,0.0005739609,0.001957235,0.00604696,0.0003577856,0.002741243,0.002257541,0.001773977,0.2457686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001777271,"about_ca_system_score_gemma":0.002898602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01865699,"about_ca_topic_score_gemma":0.0320093,"domain_scores_codex":[0.997436,0.0004301754,0.0004169564,0.0009018668,0.0005283948,0.0002865611],"domain_scores_gemma":[0.9952553,0.001043999,0.0004732189,0.001328775,0.001588298,0.0003103715],"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":[0.00009707969,0.00001638288,0.001346676,0.0006805577,0.00003459839,0.00001498164,0.00001958003,0.0001267438,0.00007197197,0.0007182182,0.9900482,0.006824982],"study_design_scores_gemma":[0.0001267916,0.0000165407,0.003136073,0.0004805972,0.00003339293,0.00005704514,0.00008040896,0.0001972491,0.0001774483,0.001450342,0.9942212,0.00002296795],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001100273,0.00009903321,0.0001068707,0.0000996818,0.0000444319,0.00002395187,0.997462,0.0002635111,0.001790428],"genre_scores_gemma":[0.0003813321,0.00009355245,0.0003524688,0.0001599561,0.00001449116,0.0001128139,0.9971001,0.00007645408,0.001708887],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.182803,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609080141467964,"score_gpt":0.4250952149123481,"score_spread":0.4090044134976684,"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."}}