{"id":"W6898541966","doi":"10.57745/svdujd","title":"Data_Second_Set_Analyses_Lunch-Morning.csv","year":2024,"lang":"en","type":"dataset","venue":"Recherche Data Gouv France","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Clinical Research Institute","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","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":[0.001712898,0.003153168,0.001776244,0.003743558,0.001079048,0.003794191,0.003160632,0.00276531,0.1981789],"category_scores_gemma":[0.01083013,0.001148492,0.002184713,0.006236813,0.0005335163,0.001827604,0.002156354,0.001939281,0.2242843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002259213,"about_ca_system_score_gemma":0.003024065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06121292,"about_ca_topic_score_gemma":0.07344543,"domain_scores_codex":[0.9984785,0.0003399676,0.0001339725,0.0004568571,0.000320714,0.0002700698],"domain_scores_gemma":[0.9951842,0.001531193,0.0003265499,0.001401108,0.001189323,0.0003675014],"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":[0.00004604859,0.00001069569,0.0003625206,0.000233113,0.00002390745,0.000005991512,0.00001271775,0.0001582364,0.00002962077,0.0002767823,0.9978897,0.0009507184],"study_design_scores_gemma":[0.0003086601,0.00001697095,0.003771472,0.0002040389,0.00002841495,0.00002265331,0.00006774224,0.000369889,0.0002553432,0.001249112,0.9936662,0.00003950428],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006028298,0.00003111135,0.00003985841,0.0000679154,0.00002578469,0.000005168754,0.9986519,0.0004944425,0.0006235121],"genre_scores_gemma":[0.0004117,0.00003844767,0.0002347032,0.0000607153,0.00001616488,0.00005724164,0.9973006,0.0003265289,0.001553974],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8018211,"threshold_uncertainty_score":0.6629746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.420866578223673,"score_gpt":0.4669424494639298,"score_spread":0.0460758712402568,"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."}}