{"id":"W2152700820","doi":"10.7202/013257ar","title":"Use of Extralinguistic Knowledge in Translation","year":2006,"lang":"en","type":"article","venue":"Meta Journal des traducteurs","topic":"Interpreting and Communication in Healthcare","field":"Health Professions","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Competence (human resources); Inference; Computer science; Comprehension; Linguistics; Natural language processing; Source text; Psychology; Knowledge base; Artificial intelligence; Social psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001220109,0.0001089771,0.000309768,0.0002245037,0.0003458383,0.00001242247,0.0001955694,0.0001026281,0.0001957874],"category_scores_gemma":[0.0003251107,0.00009024418,0.0001430293,0.0002780335,0.00008484609,0.000130487,0.00001257677,0.0008252387,0.00001772407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000644,"about_ca_system_score_gemma":0.0001516281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009290862,"about_ca_topic_score_gemma":0.001187686,"domain_scores_codex":[0.9968115,0.001669506,0.000979143,0.0001071708,0.0001395458,0.0002931379],"domain_scores_gemma":[0.9979576,0.001068918,0.0003165063,0.0002415887,0.0003516525,0.00006369626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007101588,0.001319092,0.6028455,0.002701211,0.0005920727,0.00005321444,0.03141774,0.002369515,0.007090351,0.106915,0.00646195,0.2375241],"study_design_scores_gemma":[0.002479555,0.0003741489,0.5308616,0.003276222,0.0009766539,0.0001514251,0.002247873,0.003437728,0.000848746,0.1126967,0.3418879,0.0007614659],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9441799,0.0452461,0.003964133,0.0006577275,0.001033041,0.0003417603,0.00001577795,0.00004435142,0.004517192],"genre_scores_gemma":[0.9916261,0.002030721,0.005247915,0.00005445036,0.0002086313,0.00002270123,0.000006776816,0.00001966232,0.0007830299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3354259,"threshold_uncertainty_score":0.3680051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2450704718692057,"score_gpt":0.448673067783348,"score_spread":0.2036025959141422,"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."}}