{"id":"W7014911810","doi":"","title":"Repenser le corpus littéraire québécois au temps de la diversité","year":2022,"lang":"fr","type":"other","venue":"Bibliothèque et Archives nationales du Québec (Québec government)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Field (mathematics); Subject (documents); Context (archaeology); Feature (linguistics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002118136,0.0006235975,0.0005389734,0.01359108,0.005827077,0.005252537,0.001115557,0.001276744,0.05024032],"category_scores_gemma":[0.01020501,0.0003514999,0.0003401035,0.01931565,0.00187555,0.00222319,0.001276719,0.001440566,0.00688487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02664846,"about_ca_system_score_gemma":0.03608859,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9526511,"about_ca_topic_score_gemma":0.9752511,"domain_scores_codex":[0.9983076,0.0003015452,0.00009857738,0.0002150099,0.0008656633,0.0002116531],"domain_scores_gemma":[0.9930826,0.001590595,0.0002581718,0.0004447464,0.004336427,0.0002874031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001763196,0.00003037883,0.004292714,0.0005904918,0.00003829072,0.0005401373,0.01023136,0.0004360974,0.001795534,0.07635828,0.782843,0.1226674],"study_design_scores_gemma":[0.000008824543,0.00000378451,0.00931356,0.0002146949,0.00001451479,0.00007894056,0.001637064,0.0001408754,0.0004982167,0.001001435,0.9870744,0.00001376276],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0619643,0.02969603,0.009711372,0.03776323,0.005666473,0.0005574144,0.1109195,0.001616303,0.7421053],"genre_scores_gemma":[0.1716287,0.008652446,0.01254014,0.003249388,0.0009587556,0.0006147081,0.03974544,0.002057546,0.7605528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05024032,"threshold_uncertainty_score":0.193349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007423591519504739,"score_gpt":0.2100590031354198,"score_spread":0.2026354116159151,"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."}}