{"id":"W4235859144","doi":"10.1515/iupac.88.0566","title":"Cardiac","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Data mining; Philosophy","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":[0.001613655,0.001412441,0.001370235,0.003662257,0.001014462,0.003788257,0.002464762,0.001712617,0.2948747],"category_scores_gemma":[0.01776659,0.0005738671,0.001630811,0.007048267,0.0003998749,0.003117525,0.002493042,0.001700251,0.2785705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001800383,"about_ca_system_score_gemma":0.003423744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01718496,"about_ca_topic_score_gemma":0.02650247,"domain_scores_codex":[0.9971621,0.0004600142,0.0005742108,0.0008732749,0.0006312965,0.0002990554],"domain_scores_gemma":[0.9921881,0.001940226,0.0008124243,0.001693873,0.002931252,0.0004341175],"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":[0.00009245815,0.00001062706,0.000870435,0.0009203202,0.00001984032,0.00001363157,0.0000219591,0.00006235744,0.00004652166,0.0008776222,0.989205,0.007859197],"study_design_scores_gemma":[0.0001082617,0.00001329796,0.002907667,0.0008846019,0.00002215562,0.00005222209,0.00007633626,0.00009061117,0.0001124712,0.001534848,0.9941761,0.00002144552],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001179126,0.0001631257,0.0001291659,0.0001954473,0.00008478683,0.00004292773,0.9940731,0.0002889455,0.0049047],"genre_scores_gemma":[0.0005519208,0.0002182847,0.0003790302,0.0003367685,0.00003515374,0.0002010001,0.9941804,0.0001344092,0.003963035],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7051253,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0212591636394016,"score_gpt":0.4564819075659626,"score_spread":0.435222743926561,"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."}}