{"id":"W4244979934","doi":"10.1515/iupac.79.1318","title":"Gavage","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001264854,0.0009383853,0.001149609,0.0005856018,0.0001555413,0.0001203882,0.001213592,0.0007447063,0.02641379],"category_scores_gemma":[0.001548151,0.0006989329,0.0003463341,0.000423874,0.0003542255,0.0001821262,0.0004553568,0.0009238601,0.0005450002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001522196,"about_ca_system_score_gemma":0.001726886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001659439,"about_ca_topic_score_gemma":0.001847436,"domain_scores_codex":[0.9941693,0.0002225214,0.0007784717,0.00102799,0.00279002,0.001011699],"domain_scores_gemma":[0.9954762,0.0001619239,0.0005803817,0.002478229,0.0008833297,0.0004199687],"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":[0.0003079637,0.0002980634,0.000003190559,0.0001342185,0.0001955361,0.0003273294,0.000005220406,3.15348e-7,0.0000587717,0.000008797376,0.9972459,0.001414664],"study_design_scores_gemma":[0.001622333,0.0002039306,0.00001542547,0.0006705006,0.0002475482,0.00004266205,0.00000886159,9.985068e-7,0.0000401326,0.000167574,0.9960477,0.0009323659],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002165908,0.0009532984,0.00004304804,0.0003042836,0.001285592,0.0004809765,0.9962627,0.0003825293,0.0002658862],"genre_scores_gemma":[0.000002902034,0.0005276153,0.00005157645,0.0002843491,0.002543244,0.00002621666,0.9946461,0.000291075,0.001626925],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02586879,"threshold_uncertainty_score":0.9995462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01670694009396108,"score_gpt":0.4271957960469961,"score_spread":0.4104888559530351,"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."}}