{"id":"W4236795082","doi":"10.1515/iupac.88.0569","title":"Cardiogenic","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Academic Writing and Publishing","field":"Arts and Humanities","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":[],"consensus_categories":[],"category_scores_codex":[0.001025195,0.00137711,0.00124399,0.003446081,0.0007525696,0.002372227,0.001640837,0.001554485,0.08897413],"category_scores_gemma":[0.009848149,0.0004766342,0.001589613,0.004972375,0.0003720618,0.001822826,0.001803039,0.001871706,0.06532132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008631628,"about_ca_system_score_gemma":0.001854946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009608322,"about_ca_topic_score_gemma":0.02029892,"domain_scores_codex":[0.9986143,0.000210583,0.0003655833,0.0004086245,0.0002669783,0.0001338331],"domain_scores_gemma":[0.9958741,0.001223555,0.0008078585,0.0009472313,0.0009224467,0.0002247964],"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":[0.0004083648,0.0000370098,0.006520208,0.005466685,0.0001329987,0.00009915148,0.00006823796,0.0002590889,0.000308324,0.00149536,0.9620803,0.02312424],"study_design_scores_gemma":[0.0002772317,0.00004253899,0.01986164,0.003095177,0.0001526403,0.0003708035,0.0001399208,0.0002154829,0.000342347,0.002362566,0.973084,0.00005563989],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004595333,0.000665351,0.000216955,0.0001593461,0.0001010733,0.00004900129,0.9944624,0.0002242755,0.003661951],"genre_scores_gemma":[0.001621453,0.0007504679,0.0007606887,0.0003505847,0.00006077166,0.0002758076,0.9934911,0.00008922027,0.002599814],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08897413,"threshold_uncertainty_score":0.2976481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03486916263496167,"score_gpt":0.4023474267778774,"score_spread":0.3674782641429158,"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."}}