{"id":"W4233711808","doi":"10.1515/iupac.88.0963","title":"Karyolysis","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Neurological and metabolic disorders","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); 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":[0.001237184,0.001364798,0.001515486,0.003702094,0.0007487996,0.002593582,0.001842774,0.001291749,0.1211376],"category_scores_gemma":[0.01184912,0.0004778898,0.001593627,0.004789555,0.0003545137,0.001550292,0.001902561,0.001541889,0.0687848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001192926,"about_ca_system_score_gemma":0.00231188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009066036,"about_ca_topic_score_gemma":0.01604865,"domain_scores_codex":[0.9984562,0.0001970528,0.0004698059,0.0004485481,0.0003021898,0.0001261766],"domain_scores_gemma":[0.9951987,0.001360865,0.000892197,0.00110472,0.001224543,0.0002189931],"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":[0.0004329268,0.00002385293,0.005903601,0.003918043,0.0001299563,0.0001190249,0.00004051608,0.0002680526,0.0002873348,0.001203092,0.9608297,0.02684391],"study_design_scores_gemma":[0.0003400351,0.00003165559,0.01668275,0.00217867,0.0001212157,0.0005453007,0.00008029777,0.0002055986,0.000502796,0.002757943,0.9765044,0.00004936079],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004508848,0.0007652598,0.0002897185,0.0001802772,0.00008322585,0.00007594447,0.9934762,0.0004420718,0.004236392],"genre_scores_gemma":[0.001803197,0.001017987,0.001032132,0.0004160175,0.00004822007,0.0003276992,0.992152,0.0001311287,0.003071628],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1211376,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02216279315282866,"score_gpt":0.4460586018700019,"score_spread":0.4238958087171733,"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."}}