{"id":"W7091417498","doi":"10.34847/nkl.fd42j3fw","title":"[Debogue tes humanités] IA et la correction textuelle automatique : quels outils et quelles limites ?","year":2025,"lang":"fr","type":"other","venue":"Open MIND","topic":"Cultural Insights and Digital Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Matching (statistics); Identification (biology); Information system","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":[0.01018567,0.0006849634,0.0003600322,0.003512141,0.006491657,0.01426561,0.001952844,0.002982642,0.03086396],"category_scores_gemma":[0.05479748,0.0004191937,0.0004521734,0.003072639,0.01509838,0.009516631,0.003589679,0.004037655,0.005795428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01009963,"about_ca_system_score_gemma":0.01202685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1232554,"about_ca_topic_score_gemma":0.1240612,"domain_scores_codex":[0.9865936,0.007583746,0.0005947336,0.001097563,0.003530165,0.0006001036],"domain_scores_gemma":[0.9540011,0.02250551,0.002920929,0.007258923,0.01229468,0.00101882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001366655,0.00006326925,0.003980344,0.0005925219,0.00002895417,0.0005012888,0.07851171,0.0007222236,0.002308584,0.4329365,0.1771018,0.3031162],"study_design_scores_gemma":[0.00001594429,0.00002418791,0.003909273,0.0005119152,0.00001924023,0.0005157481,0.01447306,0.0007787765,0.002327252,0.03985585,0.937505,0.00006382694],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.05298655,0.01355382,0.07324611,0.1956178,0.01045686,0.0002912361,0.001027096,0.002875265,0.6499453],"genre_scores_gemma":[0.6073505,0.01000671,0.03415502,0.01311309,0.003368974,0.0002289808,0.0006622381,0.002401643,0.3287129],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1232554,"threshold_uncertainty_score":0.2450759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1540047267680881,"score_gpt":0.3756122952772465,"score_spread":0.2216075685091584,"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."}}