{"id":"W4242576855","doi":"10.7202/1017686ar","title":"Curieux corpus","year":2013,"lang":"fr","type":"article","venue":"Revue de Bibliothèque et Archives nationales du Québec","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bibliothèque et Archives nationales du Québec","funders":"","keywords":"Computer science; Natural language processing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001803926,0.001797924,0.001604918,0.01381023,0.005419166,0.006223789,0.002483616,0.001553837,0.2346292],"category_scores_gemma":[0.01005607,0.0007303892,0.0008870194,0.01875172,0.001661392,0.002731735,0.002597102,0.002277157,0.07183791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02530694,"about_ca_system_score_gemma":0.02489914,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8436154,"about_ca_topic_score_gemma":0.8645125,"domain_scores_codex":[0.9972104,0.0005579449,0.0002354559,0.0006428534,0.0009572501,0.000396059],"domain_scores_gemma":[0.9947599,0.001032,0.0001949102,0.0007687482,0.003032794,0.0002116517],"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":[0.0001839498,0.00003099662,0.0009105433,0.0008364472,0.00003423117,0.0001433511,0.001130906,0.000259854,0.0005986782,0.0297191,0.9241713,0.04198058],"study_design_scores_gemma":[0.00001887684,0.000002901829,0.002468162,0.0001861715,0.00001328966,0.00005465963,0.0002681148,0.0001145675,0.0003206299,0.001476004,0.9950611,0.00001553506],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.005353014,0.003902368,0.003362939,0.002919927,0.0009735788,0.0003431648,0.6472666,0.003265998,0.3326125],"genre_scores_gemma":[0.05180128,0.003423418,0.009040753,0.001416807,0.0004670407,0.0009223256,0.6318221,0.004191102,0.2969152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8436154,"threshold_uncertainty_score":0.7849129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0151343698270479,"score_gpt":0.2460368407875431,"score_spread":0.2309024709604952,"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."}}