{"id":"W215104783","doi":"","title":"La mise en evidence textuelle: d'ou venons nous et ou allons nous (Textual Enhancement: Where Have We Been and Where Are We Going)?.","year":2002,"lang":"fr","type":"article","venue":"Canadian Modern Language Review/ La Revue canadienne des langues vivantes","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","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":[0.02531722,0.0006073803,0.0009738068,0.005040354,0.002075329,0.01028407,0.002149526,0.003477624,0.01079523],"category_scores_gemma":[0.09868684,0.0004833185,0.0004814216,0.003122848,0.006311206,0.01574245,0.002297072,0.003623873,0.00516955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004093966,"about_ca_system_score_gemma":0.009200181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01788045,"about_ca_topic_score_gemma":0.03226437,"domain_scores_codex":[0.9888113,0.006048924,0.0009031481,0.0009373225,0.002978617,0.0003207691],"domain_scores_gemma":[0.9074022,0.05790748,0.006169427,0.004477907,0.02227307,0.001769977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004532191,0.00006321377,0.003850177,0.004873248,0.0001428199,0.0008293074,0.006045138,0.0003003658,0.006014579,0.06475002,0.1542984,0.7583796],"study_design_scores_gemma":[0.00006122361,0.00009830925,0.00517816,0.005440143,0.0002330228,0.002087986,0.009910489,0.001451617,0.009384586,0.06094235,0.9050941,0.0001178443],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02473373,0.3572934,0.1446219,0.3864103,0.01405282,0.0002628147,0.003065997,0.002027103,0.06753205],"genre_scores_gemma":[0.3599051,0.2399497,0.2615229,0.04259524,0.01298078,0.0003461404,0.002871058,0.001149868,0.07867926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02531722,"threshold_uncertainty_score":0.1338919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0322725252733055,"score_gpt":0.271803058608003,"score_spread":0.2395305333346975,"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."}}