{"id":"W6898792868","doi":"10.57745/pyymtg","title":"2024_0211.zip","year":2025,"lang":"en","type":"dataset","venue":"Recherche Data Gouv France","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","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":[0.001308827,0.003025796,0.001869368,0.004720237,0.00126202,0.004050173,0.003483762,0.003323352,0.2479132],"category_scores_gemma":[0.008163855,0.001071181,0.001488661,0.007711923,0.0006447392,0.002430885,0.002314321,0.00212067,0.3033956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002754341,"about_ca_system_score_gemma":0.003088481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05435431,"about_ca_topic_score_gemma":0.06694065,"domain_scores_codex":[0.9985235,0.0003012626,0.0001183661,0.000463757,0.0003013986,0.000291768],"domain_scores_gemma":[0.9965856,0.001155999,0.000209543,0.0008218859,0.0008872678,0.0003396564],"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":[0.00003581412,0.000009802349,0.0001530859,0.0002029773,0.00001088825,0.000007366461,0.00001048804,0.0001197323,0.0000367122,0.0003452761,0.9982658,0.0008021985],"study_design_scores_gemma":[0.0002399781,0.00001417306,0.001386189,0.0001793943,0.00001251597,0.0000264615,0.00005738798,0.0003469701,0.0002145622,0.001188998,0.9963075,0.00002606196],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004167017,0.0000317679,0.0000333319,0.00006510107,0.00001972243,0.000005134316,0.9985417,0.0004077791,0.0008537285],"genre_scores_gemma":[0.000219965,0.00003179444,0.0001471258,0.00005900855,0.000008527344,0.0000326503,0.9983069,0.0001814756,0.001012603],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7520868,"threshold_uncertainty_score":0.8293524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3765694523694549,"score_gpt":0.4680781012174098,"score_spread":0.09150864884795484,"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."}}