{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003039929,0.0004213645,0.001115219,0.0001755764,0.0001588631,0.0000537868,0.0004156146,0.0004322934,0.005157932],"category_scores_gemma":[0.001355464,0.0003006079,0.0003941745,0.0001046845,0.0002508108,0.00004723512,0.0001905398,0.0008282816,0.00001543352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005968838,"about_ca_system_score_gemma":0.0006175707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000139593,"about_ca_topic_score_gemma":0.0001987898,"domain_scores_codex":[0.9975465,0.0000465827,0.0003718645,0.0005679337,0.001037525,0.0004295769],"domain_scores_gemma":[0.997598,0.000041714,0.000270192,0.001462373,0.0003046631,0.000323036],"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":[0.0003795611,0.0005059057,0.00003860745,0.0001715236,0.0001834026,0.0004756892,0.000002463891,2.401134e-7,0.000008464061,0.000003455583,0.9882865,0.00994417],"study_design_scores_gemma":[0.001562352,0.0005090528,0.0007250375,0.0001348652,0.00085887,0.00006484939,0.000006134919,0.000001613136,0.000006324464,0.0000991958,0.9957484,0.0002832802],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005747761,0.001597856,0.00000746174,0.003121184,0.0007380825,0.0003509816,0.9930027,0.0000768613,0.0005301345],"genre_scores_gemma":[0.00004021409,0.003156882,0.00003944676,0.002708986,0.0009972592,0.00001622727,0.9900837,0.00003092951,0.002926334],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009660889,"threshold_uncertainty_score":0.9999446,"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."}}