{"id":"W2067366489","doi":"10.7202/1027474ar","title":"Evidence of Parallel Processing During Translation","year":2014,"lang":"en","type":"article","venue":"Meta Journal des traducteurs","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Source text; Reading (process); Natural language processing; Literal translation; Eye tracking; Congruence (geometry); Machine translation; Artificial intelligence; Linguistics; Translation (biology); Danish; Target text; Example-based machine translation; 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":[0.0009871699,0.0001481602,0.0002808231,0.0001850584,0.0002031434,0.000240306,0.0009126484,0.00004925245,0.000007815416],"category_scores_gemma":[0.0002065376,0.000110449,0.0001580634,0.0004366997,0.00009212423,0.0020935,0.00003844031,0.0002962348,0.000001443737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002822892,"about_ca_system_score_gemma":0.00004714893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004279464,"about_ca_topic_score_gemma":0.000002257739,"domain_scores_codex":[0.9985591,0.0001900545,0.0003880849,0.0002228394,0.0004003811,0.0002395696],"domain_scores_gemma":[0.9990621,0.00009704928,0.0003032369,0.0002508193,0.0001946259,0.00009220919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002309836,0.0000324354,0.0002042628,0.0002583709,0.00006416378,0.00001263141,0.0006502277,0.0001187271,0.07940763,0.003644791,0.00001085198,0.9155728],"study_design_scores_gemma":[0.0009780349,0.0005389727,0.006513288,0.002321229,0.0006441097,0.002883306,0.0000210155,0.02411881,0.4341179,0.5259825,0.0009512731,0.0009295843],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02934376,0.141824,0.8281398,0.000405621,0.00007476255,0.00005662664,1.202896e-7,0.0001303352,0.0000249423],"genre_scores_gemma":[0.5144662,0.001185518,0.4842471,0.00002470458,0.0000491004,0.00000236058,7.479721e-8,0.000007357533,0.00001756869],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9146432,"threshold_uncertainty_score":0.4503978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05663297699258963,"score_gpt":0.2992657555134165,"score_spread":0.2426327785208269,"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."}}