{"id":"W4230480676","doi":"10.1515/iupac.79.2035","title":"Specific Death Rate","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Hazard; Computer science; Toxicology; Multidisciplinary approach; Chemistry; Philosophy; Biology; Political science; Linguistics; Law","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":[0.00153425,0.001179081,0.001838829,0.00397491,0.0004052331,0.00184126,0.001903404,0.00119822,0.07178382],"category_scores_gemma":[0.01236427,0.0004192631,0.002859238,0.005934365,0.0002363719,0.001273484,0.001027509,0.002164543,0.04820105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001845412,"about_ca_system_score_gemma":0.001787313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02330102,"about_ca_topic_score_gemma":0.0229947,"domain_scores_codex":[0.9974016,0.0003469768,0.0006169727,0.0009017063,0.0005171986,0.0002155303],"domain_scores_gemma":[0.9954603,0.00117377,0.0009964855,0.0006421332,0.00152449,0.0002027093],"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":[0.0003379712,0.00003329312,0.01364546,0.002516754,0.0003574739,0.00004175161,0.00002982573,0.001108743,0.00007735643,0.001302927,0.9667003,0.0138482],"study_design_scores_gemma":[0.0009637414,0.0001059673,0.04861115,0.001866972,0.0005102212,0.0004286507,0.000127319,0.001557315,0.000378148,0.002622008,0.942735,0.00009352912],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004638056,0.0003871715,0.0001268705,0.0001017774,0.00007083669,0.00003398399,0.9969086,0.0001090633,0.001797902],"genre_scores_gemma":[0.003185714,0.0005690935,0.0003283595,0.0002334447,0.00007222778,0.0002680125,0.9925141,0.00006293358,0.002766139],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07178382,"threshold_uncertainty_score":0.2401408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05274021720858207,"score_gpt":0.4258230743347597,"score_spread":0.3730828571261777,"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."}}