{"id":"W4235828529","doi":"10.1515/iupac.79.0853","title":"Arteriosclerosis","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Computational Drug Discovery Methods","field":"Computer Science","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; 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":[0.0007435813,0.001393466,0.001854857,0.002886607,0.0006921118,0.002082894,0.002227661,0.00175986,0.06627793],"category_scores_gemma":[0.006237741,0.0004374168,0.001389642,0.004011143,0.0002569917,0.001004944,0.001244201,0.002104222,0.04829983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007548612,"about_ca_system_score_gemma":0.001803023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007319375,"about_ca_topic_score_gemma":0.01641518,"domain_scores_codex":[0.9989791,0.0001166546,0.0001912119,0.0003884,0.0002173433,0.0001073204],"domain_scores_gemma":[0.9979507,0.0005189272,0.0003683436,0.0005170709,0.0004468458,0.0001981394],"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":[0.0003867449,0.00004115672,0.005220084,0.002011391,0.0001305024,0.00007941113,0.00001811024,0.0002402401,0.0001678692,0.0006001286,0.9783755,0.01272894],"study_design_scores_gemma":[0.001207088,0.00008716146,0.03362104,0.002143794,0.00036495,0.0009654472,0.00008986893,0.0008823487,0.000725311,0.003667527,0.9561611,0.0000843628],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005399947,0.0004194605,0.0001137777,0.0001077413,0.00005681034,0.00004271763,0.9967333,0.0001558752,0.001830272],"genre_scores_gemma":[0.001461087,0.0003325223,0.0004680689,0.0001860185,0.00004207693,0.0002687683,0.9958401,0.00003541193,0.001366],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06627793,"threshold_uncertainty_score":0.2217217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02257170230029138,"score_gpt":0.4178454124659309,"score_spread":0.3952737101656396,"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."}}