{"id":"W4243660610","doi":"10.1515/iupac.79.1527","title":"Larynx","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; Chemical nomenclature; Computer science; Hazard; Toxicology; Chemistry; Philosophy; Biology; 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":[],"consensus_categories":[],"category_scores_codex":[0.000593894,0.001457271,0.001423743,0.002073671,0.0006395165,0.001761825,0.001905503,0.001482654,0.1513262],"category_scores_gemma":[0.005827593,0.0003806776,0.001377382,0.002882406,0.0002656491,0.001268515,0.001550679,0.001068767,0.144556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0010564,"about_ca_system_score_gemma":0.001903278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01683459,"about_ca_topic_score_gemma":0.03079239,"domain_scores_codex":[0.999281,0.0001092027,0.0001126007,0.0002803092,0.0001320742,0.00008488359],"domain_scores_gemma":[0.9984924,0.0004811796,0.0001899866,0.0003061184,0.0004040628,0.000126267],"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":[0.000257714,0.00001807501,0.002402925,0.001376519,0.0000644156,0.00006980787,0.00001736144,0.0002020946,0.0001381467,0.0004252427,0.983278,0.01174975],"study_design_scores_gemma":[0.0002568505,0.00003755254,0.009679527,0.001107238,0.00009669335,0.0002700096,0.00008590134,0.0002837017,0.0003261733,0.001393249,0.9864299,0.00003313125],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002214831,0.0003039094,0.00008742686,0.00008218949,0.00004334264,0.0000207824,0.9974642,0.0001900502,0.001586712],"genre_scores_gemma":[0.0009651646,0.0002815169,0.0002976345,0.0001773011,0.00002323334,0.0001479182,0.9958047,0.0000509413,0.002251598],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1513262,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01252040221389914,"score_gpt":0.3885157857642692,"score_spread":0.37599538355037,"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."}}