{"id":"W4398501806","doi":"10.7910/dvn/k0oyqf/xdddqe","title":"senate_parameterization.tab","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","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":["insufficient_payload"],"category_scores_codex":[0.0000592476,0.0002781013,0.0002566736,0.0001534418,0.00003892478,0.0001197448,0.000365953,0.0002573036,0.01491718],"category_scores_gemma":[0.00002974229,0.0002872592,0.00005226041,0.0001031568,0.00001698777,0.0002267214,0.0001073352,0.0002553467,0.1236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005221746,"about_ca_system_score_gemma":0.00002398845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002446124,"about_ca_topic_score_gemma":0.00000596916,"domain_scores_codex":[0.9990709,0.00001419338,0.0002266758,0.0002658837,0.0001931312,0.0002291579],"domain_scores_gemma":[0.9990002,0.00002432913,0.00006213701,0.0008155375,0.00003071428,0.00006708657],"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":[0.000003243884,0.000008204137,4.820681e-7,0.0005129162,0.00003729214,0.000007096348,0.000004962383,0.09094886,0.0000040232,0.000001442818,0.9083368,0.0001346838],"study_design_scores_gemma":[0.0001632243,0.000009006803,0.000005023858,0.00005650316,0.00005514996,0.000004080568,0.000003313852,0.01227152,0.0001171441,0.000003913449,0.9869804,0.0003307248],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000007220998,0.000002080798,0.003329159,6.219005e-7,0.001487686,0.0001963915,0.9944064,0.0001700433,0.0004003993],"genre_scores_gemma":[0.00002868932,0.0008411968,0.0005448372,0.00006887779,0.000204514,0.00001719707,0.9978393,0.00004282482,0.0004126146],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1086828,"threshold_uncertainty_score":0.999958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01262554602844977,"score_gpt":0.2129671170580989,"score_spread":0.2003415710296492,"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."}}