{"id":"W6936200719","doi":"10.57745/ybkcb5","title":"2024_0221.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":[0.001271363,0.002936928,0.001843156,0.004697755,0.00123504,0.003959706,0.003376465,0.003233666,0.2531471],"category_scores_gemma":[0.008055394,0.001043439,0.001453331,0.007647166,0.000620932,0.002408803,0.002266621,0.002040007,0.3049687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002714626,"about_ca_system_score_gemma":0.0030131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05464531,"about_ca_topic_score_gemma":0.06617914,"domain_scores_codex":[0.998603,0.0002854503,0.0001139698,0.0004374816,0.0002850697,0.000275061],"domain_scores_gemma":[0.9966864,0.001125621,0.0002064664,0.0007818605,0.0008676124,0.000332089],"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":[0.00003690659,0.000009487839,0.000156224,0.0002080718,0.00001116784,0.000007424461,0.00001051939,0.0001188036,0.00003674546,0.0003457333,0.9982407,0.0008182531],"study_design_scores_gemma":[0.0002364484,0.00001375909,0.001387335,0.0001777644,0.00001246153,0.00002535847,0.00005508561,0.0003277478,0.0002033245,0.001165555,0.9963698,0.00002537058],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004002339,0.00003160704,0.00003152448,0.0000635579,0.00001938214,0.000004900307,0.998577,0.0003845064,0.0008474129],"genre_scores_gemma":[0.0002211758,0.00003259463,0.0001436962,0.00005961752,0.000008693556,0.00003161296,0.9983104,0.0001763761,0.001015787],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7468529,"threshold_uncertainty_score":0.8468613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3763217684771959,"score_gpt":0.4679561163364889,"score_spread":0.09163434785929303,"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."}}