{"id":"W4247824724","doi":"10.1515/iupac.76.0349","title":"Pulmonary","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Relation (database); Hazard; Toxicology; Computer science; Medicine; Chemistry; Pharmacology; Data mining; Biology; Philosophy; Linguistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001258766,0.001609248,0.001271772,0.003673546,0.0008208405,0.003380872,0.002263295,0.001680226,0.1839061],"category_scores_gemma":[0.01086507,0.0005704807,0.001527444,0.005393636,0.0003642698,0.002588019,0.002079315,0.001533612,0.2115879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001455218,"about_ca_system_score_gemma":0.002598427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01200978,"about_ca_topic_score_gemma":0.02410331,"domain_scores_codex":[0.9978647,0.0003434335,0.0003250831,0.0008314669,0.0004452959,0.0001901116],"domain_scores_gemma":[0.9960925,0.00115654,0.0004147752,0.001003313,0.00106294,0.0002699185],"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":[0.00008912806,0.00001991572,0.001273202,0.001039484,0.00003039236,0.00002454984,0.00002687145,0.0001542513,0.0001277628,0.000801219,0.9873532,0.009060063],"study_design_scores_gemma":[0.00008751939,0.00001513855,0.002715618,0.0005668645,0.00002430996,0.00006464437,0.0000603549,0.0002004067,0.000210644,0.001390603,0.9946421,0.00002168423],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001303985,0.0001872138,0.000144646,0.0001119581,0.00004651918,0.00002402175,0.9968981,0.0004363718,0.002020803],"genre_scores_gemma":[0.0003938857,0.000151304,0.0003864574,0.0001489132,0.00001474947,0.0001071866,0.9970691,0.0001022208,0.001626209],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8160939,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01271431780641656,"score_gpt":0.3796191132562386,"score_spread":0.366904795449822,"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."}}