{"id":"W4233304996","doi":"10.1515/iupac.88.0564","title":"Caput Epididymis","year":2017,"lang":"la","type":"dataset","venue":"IUPAC Standards Online","topic":"Pharmaceutical studies and practices","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; Data mining; Philosophy","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001007254,0.00118441,0.001230501,0.00463867,0.0008041974,0.002416916,0.001725718,0.001157934,0.09467357],"category_scores_gemma":[0.008146842,0.0004527209,0.001478038,0.006736374,0.0004633914,0.001453555,0.00202383,0.001343435,0.06252227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001190024,"about_ca_system_score_gemma":0.002581596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01852212,"about_ca_topic_score_gemma":0.0320337,"domain_scores_codex":[0.9986492,0.0002192621,0.0003409146,0.0003942029,0.0002666428,0.0001296986],"domain_scores_gemma":[0.9961724,0.001198973,0.0008009466,0.0007243082,0.0008871193,0.0002162765],"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":[0.0003937088,0.0000292376,0.008181619,0.00574602,0.0001373131,0.0001287062,0.00006669833,0.000314897,0.0002754671,0.00115427,0.9596303,0.02394173],"study_design_scores_gemma":[0.0001781205,0.00003771496,0.02077716,0.002490572,0.00009659856,0.000358483,0.000116442,0.0002124888,0.0003113052,0.00108273,0.9743006,0.00003768497],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004078522,0.0006452486,0.0001296878,0.0001098515,0.00005613065,0.00004049742,0.9960424,0.0001872058,0.002381154],"genre_scores_gemma":[0.001804567,0.0007066374,0.0006654652,0.0002683584,0.00003920606,0.0002274967,0.9942542,0.00005588893,0.001978162],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9053264,"threshold_uncertainty_score":0.3167146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07152745783462158,"score_gpt":0.5425047182027382,"score_spread":0.4709772603681166,"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."}}