{"id":"W4247708601","doi":"10.1515/iupac.88.0719","title":"Edema","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Biomedical and Chemical Research","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004942621,0.0003318476,0.0008269989,0.0001451506,0.0001424253,0.00006181186,0.0005452074,0.0007852952,0.01091454],"category_scores_gemma":[0.003222862,0.0002338092,0.000259073,0.0001075541,0.0005563186,0.00003260559,0.000340586,0.001426621,0.00002592801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002786033,"about_ca_system_score_gemma":0.001650264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001434345,"about_ca_topic_score_gemma":0.00009725869,"domain_scores_codex":[0.9961846,0.00002726277,0.000363142,0.0004966844,0.002373442,0.0005548529],"domain_scores_gemma":[0.9971642,0.00008474576,0.0001514338,0.001328045,0.0005264138,0.0007450985],"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":[0.0003787062,0.0004432887,0.000009214833,0.0006978108,0.0001342851,0.000543889,0.000002033412,2.920558e-9,0.00006988388,0.000001361931,0.9863858,0.01133369],"study_design_scores_gemma":[0.001462237,0.00040094,0.00008126238,0.0008372147,0.0001936068,0.00006038194,0.000004707083,0.000003218943,0.000107616,0.0001037486,0.9965115,0.0002335802],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009669943,0.001507731,0.000008396942,0.006063328,0.0003496126,0.0002673734,0.9912406,0.00004561942,0.0004206117],"genre_scores_gemma":[0.00002835747,0.001645427,0.00004473742,0.0008437927,0.002215323,0.00001281903,0.990633,0.00002954429,0.004547062],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01110011,"threshold_uncertainty_score":0.9899896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03469842732845404,"score_gpt":0.5096718823382879,"score_spread":0.4749734550098338,"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."}}