{"id":"W4256486681","doi":"10.1515/iupac.88.1359","title":"Sphenoid Bone","year":2017,"lang":"pl","type":"dataset","venue":"IUPAC Standards Online","topic":"Medical and Biological Sciences","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Medicine; Computer science; Linguistics; Philosophy; Data mining","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":[0.0007075402,0.001343116,0.001372143,0.003554363,0.0006096904,0.002013722,0.001596345,0.001080268,0.1177194],"category_scores_gemma":[0.007502517,0.0004246311,0.001829634,0.003797299,0.0003582926,0.001745858,0.001754031,0.001325333,0.07120963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009799565,"about_ca_system_score_gemma":0.002458719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0129969,"about_ca_topic_score_gemma":0.0247355,"domain_scores_codex":[0.9991156,0.0001059858,0.000258078,0.0002637575,0.0001705892,0.00008591608],"domain_scores_gemma":[0.9974372,0.0008776287,0.0004405488,0.000529243,0.0005615235,0.0001537861],"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":[0.0005639474,0.000022417,0.006182196,0.006489658,0.0001872781,0.000241969,0.00005540054,0.0003884278,0.0004211374,0.001347476,0.9450735,0.03902649],"study_design_scores_gemma":[0.0002810853,0.00003782558,0.0157402,0.002786214,0.0001362332,0.0007850039,0.0001321528,0.0002617156,0.0004428596,0.002486315,0.9768638,0.00004641183],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005633797,0.0007597972,0.0002632867,0.0001406152,0.0000792991,0.0000486986,0.9942856,0.0002735588,0.003585844],"genre_scores_gemma":[0.002464355,0.0009608848,0.001006757,0.0002630987,0.00004225148,0.0002057348,0.9928424,0.0001057101,0.002108854],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1177194,"threshold_uncertainty_score":0.3938107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03627341322754738,"score_gpt":0.4513974337931795,"score_spread":0.4151240205656321,"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."}}