{"id":"W7030158633","doi":"","title":"L'uniformisation rÃ©gionale du droit, processus comparÃ©s et projet de l'OHADA en transport routier","year":2000,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Work (physics); Term (time); MEDLINE","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01546819,0.001143973,0.0009326502,0.004128189,0.003338424,0.0156802,0.002484554,0.002417676,0.0197129],"category_scores_gemma":[0.01785222,0.001047443,0.001468101,0.005236241,0.003036313,0.0118709,0.003126333,0.003173732,0.005071409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01880184,"about_ca_system_score_gemma":0.01786552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4214252,"about_ca_topic_score_gemma":0.2343067,"domain_scores_codex":[0.9875486,0.003816694,0.00110362,0.002821845,0.003934911,0.0007742834],"domain_scores_gemma":[0.9857006,0.003476089,0.0009406602,0.004320177,0.005103873,0.0004586178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008123019,0.0003184584,0.01126511,0.0006704275,0.00009417858,0.0002401813,0.003249255,0.01297016,0.01020702,0.217256,0.0372174,0.7056996],"study_design_scores_gemma":[0.0002573159,0.0003322143,0.03836215,0.0006792086,0.0001401537,0.0006642747,0.004915007,0.06762465,0.04029277,0.04644462,0.7999902,0.0002975035],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07395937,0.01150412,0.7594707,0.01006434,0.001475113,0.0009456069,0.005839129,0.01530156,0.1214401],"genre_scores_gemma":[0.3661632,0.007591672,0.3727657,0.001203754,0.0005524147,0.0007856648,0.01144502,0.004240416,0.2352521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4214252,"threshold_uncertainty_score":0.8379445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003671428686191954,"score_gpt":0.1644929105910733,"score_spread":0.1608214819048814,"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."}}