{"id":"W6917490572","doi":"10.57745/kfrepn","title":"Up to 48h of recovery.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":"Key (lock); Matching (statistics); Component (thermodynamics); Measure (data warehouse)","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":[0.0009661696,0.003036638,0.002233917,0.002150951,0.0008369357,0.002329805,0.002416364,0.002954158,0.1329438],"category_scores_gemma":[0.004890292,0.0008184361,0.001860584,0.00398917,0.0003819051,0.001337829,0.001728201,0.001693506,0.165273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0015146,"about_ca_system_score_gemma":0.002194263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02531643,"about_ca_topic_score_gemma":0.03679439,"domain_scores_codex":[0.9990971,0.0001129307,0.0001120005,0.0003348652,0.0001917085,0.0001513509],"domain_scores_gemma":[0.9980141,0.0006072992,0.0001539546,0.0005138945,0.000551781,0.0001589666],"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":[0.0001622539,0.0000279298,0.0006107107,0.0007082763,0.00003035026,0.00001937307,0.00001255808,0.0002019022,0.000205737,0.0001777937,0.9952646,0.00257853],"study_design_scores_gemma":[0.0005847944,0.00006443929,0.00676626,0.0004467439,0.00005919719,0.00008950981,0.00006260017,0.0005511164,0.0009078322,0.001229941,0.9891784,0.00005922017],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005961759,0.00004617385,0.00002845977,0.00003834681,0.00002366056,0.000006607564,0.9991766,0.0002994262,0.0003210601],"genre_scores_gemma":[0.0002559759,0.00004185017,0.0001553676,0.00004610303,0.000007679178,0.00003716847,0.998874,0.00007236608,0.0005094167],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8670562,"threshold_uncertainty_score":0.4447414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3644856749875672,"score_gpt":0.45625710302724,"score_spread":0.09177142803967281,"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."}}