{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","open_science","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity","insufficient_payload"],"category_scores_codex":[0.00750372,0.001008386,0.001525168,0.0007106149,0.00006234161,0.0001879884,0.00867598,0.002143485,0.001503157],"category_scores_gemma":[0.01070739,0.001023937,0.0002285973,0.003272866,0.0001934783,0.0006508772,0.004155094,0.004375505,0.2511617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000841852,"about_ca_system_score_gemma":0.001179784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002198728,"about_ca_topic_score_gemma":0.001579151,"domain_scores_codex":[0.9925658,0.00137065,0.001272155,0.002617965,0.001216072,0.0009573306],"domain_scores_gemma":[0.9847888,0.001604675,0.0005926638,0.01234919,0.0002883079,0.0003763785],"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":[0.000154865,0.0001070278,0.000003239724,0.001666324,0.0003874236,0.00005744246,0.00007043011,0.00001189635,0.0006071897,0.000005317705,0.9915664,0.005362418],"study_design_scores_gemma":[0.0002988029,0.0001208789,0.00001665392,0.001832042,0.0004586795,0.00002446618,0.00003568429,0.00005260039,0.0004203065,0.0004090244,0.995315,0.001015873],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003577617,0.005376766,0.0001063048,0.0002869147,0.003377352,0.001027107,0.9890763,0.0002577694,0.00045574],"genre_scores_gemma":[0.000001079003,0.00290765,0.006087037,0.0005214617,0.0008280345,0.0001152093,0.9833232,0.0003591847,0.005857106],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2496586,"threshold_uncertainty_score":0.9994096,"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."}}