{"id":"W4254441527","doi":"10.1515/iupac.79.0868","title":"Atherosclerosis","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Lipoproteins and Cardiovascular Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Library science; 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":[0.001169038,0.001502872,0.001517356,0.002465143,0.0007885263,0.00290626,0.00228345,0.001641305,0.1512982],"category_scores_gemma":[0.009881386,0.0004447035,0.001917865,0.003865807,0.0002330076,0.001486645,0.001478992,0.001509896,0.151461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001061506,"about_ca_system_score_gemma":0.002364586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01169974,"about_ca_topic_score_gemma":0.02063133,"domain_scores_codex":[0.9984868,0.0002452566,0.0002620336,0.0005658193,0.0002835553,0.0001566633],"domain_scores_gemma":[0.9969385,0.0007243234,0.0004295341,0.0007529962,0.0009269726,0.0002277479],"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":[0.0003036973,0.00003058034,0.003159424,0.001323771,0.00009029696,0.00003795244,0.00002419238,0.0001216668,0.00007565012,0.0006451319,0.9813017,0.01288589],"study_design_scores_gemma":[0.0004007147,0.00003390167,0.008355671,0.0009356971,0.0001385318,0.000181751,0.00007101545,0.000201666,0.0001992834,0.001905786,0.9875423,0.00003367844],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002873392,0.000346345,0.0001560344,0.0001805531,0.00007940839,0.00004850718,0.9950238,0.0003299177,0.003548231],"genre_scores_gemma":[0.001043771,0.0003301514,0.0005146054,0.0003947937,0.00005946356,0.0002526149,0.9936057,0.00009229055,0.003706543],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1512982,"threshold_uncertainty_score":0.5061427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01905542860520653,"score_gpt":0.3896748692477298,"score_spread":0.3706194406425233,"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."}}