{"id":"W4387896177","doi":"10.54254/2755-2721/13/20230734","title":"The development and advance of machine translation","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Machine translation; Computer science; Translation (biology); Example-based machine translation; Artificial intelligence; Evaluation of machine translation; Natural language processing; Key (lock); Machine translation software usability; Machine learning; Transfer-based machine translation; Encoder","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009352875,0.00003960497,0.00003825233,0.00003541225,0.00005901411,0.00002482584,0.00007876354,0.00001079359,6.479339e-8],"category_scores_gemma":[0.000004131492,0.00003037212,0.000004053045,0.0001471592,0.000009810175,0.00005283197,0.00003891884,0.00003423985,3.766946e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003828847,"about_ca_system_score_gemma":0.000009805868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.867623e-7,"about_ca_topic_score_gemma":3.06304e-7,"domain_scores_codex":[0.9997064,0.000001574849,0.00007986696,0.00007639211,0.00007990847,0.00005588733],"domain_scores_gemma":[0.9997968,0.0001242084,0.00001805878,0.00003555373,0.00001215286,0.00001318952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001529239,0.000001707348,0.00001164748,0.00002894816,0.000005080683,4.404606e-7,0.0004932317,0.01825817,0.001282405,0.3308465,0.000003590582,0.6490668],"study_design_scores_gemma":[0.00009194472,0.000004488894,0.001451335,0.00001332538,8.436109e-7,0.000003078203,0.000008305044,0.9627843,0.003122937,0.03163201,0.0008210987,0.00006630456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009540458,0.00160371,0.9884129,0.0001623021,0.00001685461,0.00004456576,3.269662e-7,0.0001742572,0.00004467362],"genre_scores_gemma":[0.6025082,0.000008475531,0.3974615,0.000006749718,0.000003267933,0.000005394791,0.000001822445,0.000001779183,0.000002787765],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9445262,"threshold_uncertainty_score":0.1238539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006754346068365035,"score_gpt":0.2196955278117962,"score_spread":0.2129411817434312,"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."}}