{"id":"W2221532354","doi":"","title":"Surmonter l'interférence culturelle et linguistique à l'aide de CALL","year":2006,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Computer science; Linguistics; Process (computing); Cognitive science; Artificial intelligence; Psychology; Philosophy; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.004196846,0.0008661357,0.0005374528,0.0007250824,0.00180542,0.006391354,0.0009679787,0.002031585,0.004096201],"category_scores_gemma":[0.01192065,0.0003480681,0.0005875046,0.0006342463,0.002898477,0.004762703,0.004848648,0.002580257,0.001304353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646583,"about_ca_system_score_gemma":0.002622921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01002007,"about_ca_topic_score_gemma":0.008642135,"domain_scores_codex":[0.9928997,0.003105893,0.0002384574,0.0007530912,0.002513641,0.0004891652],"domain_scores_gemma":[0.9937729,0.003604216,0.000467716,0.00066907,0.001199293,0.0002868778],"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":[0.000627567,0.0004838966,0.02863675,0.001115188,0.0002193078,0.001620718,0.02934355,0.008506823,0.0870612,0.2368851,0.007775365,0.5977244],"study_design_scores_gemma":[0.0001735308,0.001021314,0.0509001,0.0005871411,0.0006016893,0.006128531,0.03149263,0.1266533,0.1541305,0.1196019,0.5081461,0.0005633035],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3886033,0.004986627,0.425867,0.01386221,0.0009472635,0.0002022067,0.0001230653,0.001927133,0.1634812],"genre_scores_gemma":[0.9062203,0.001788082,0.07061844,0.001243299,0.0003674843,0.0001022676,0.0001121888,0.0002173801,0.01933063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01002007,"threshold_uncertainty_score":0.02219534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01671232192594397,"score_gpt":0.2665148099085788,"score_spread":0.2498024879826349,"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."}}