{"id":"W7043425937","doi":"","title":"Sentence processing in translation: a corpus approach","year":2023,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Translation Studies and Practices","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Sentence; Sentence processing; Feature (linguistics); Field (mathematics)","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":[0.004659126,0.001386281,0.001319617,0.00440878,0.002005162,0.005228087,0.001831805,0.001935395,0.01884109],"category_scores_gemma":[0.0171588,0.001266209,0.001366891,0.006875679,0.001298508,0.006451134,0.002616235,0.002547287,0.006784654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195828,"about_ca_system_score_gemma":0.002631918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005634445,"about_ca_topic_score_gemma":0.005056156,"domain_scores_codex":[0.9949805,0.003098966,0.0004345307,0.0006671409,0.0006638904,0.0001550967],"domain_scores_gemma":[0.9842256,0.01137289,0.0004125779,0.001235118,0.002581903,0.0001719793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001233912,0.0005282374,0.002199884,0.002959477,0.0003247009,0.001394764,0.003780194,0.02389207,0.05355774,0.08230461,0.0606365,0.7671879],"study_design_scores_gemma":[0.0003446589,0.0008033607,0.007599885,0.0007958746,0.0007459358,0.001679033,0.004671692,0.545558,0.07392882,0.1579288,0.2056424,0.000301466],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04050415,0.003502309,0.9037831,0.002954274,0.0008590528,0.0008996667,0.009623189,0.008043527,0.02983078],"genre_scores_gemma":[0.2382455,0.002840257,0.7188554,0.0005130722,0.0009976497,0.001392421,0.02250201,0.003141492,0.01151212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01884109,"threshold_uncertainty_score":0.06302971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05110754300887283,"score_gpt":0.2563724515709963,"score_spread":0.2052649085621234,"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."}}