{"id":"W1484029852","doi":"10.1007/11424918_43","title":"English to Chinese Translation of Prepositions","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Machine translation; Natural language processing; WordNet; Artificial intelligence; Focus (optics); Task (project management); Set (abstract data type); Process (computing); Semantic interpretation; Example-based machine translation; Translation (biology); 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":[0.0004049576,0.0009630261,0.0004773278,0.000666358,0.0006350015,0.001294516,0.0005005174,0.0003788278,0.03340487],"category_scores_gemma":[0.001243045,0.0003506842,0.0003962393,0.001315975,0.0006038843,0.001261302,0.0008132569,0.0008144819,0.01712606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006967706,"about_ca_system_score_gemma":0.001548134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003477145,"about_ca_topic_score_gemma":0.00337698,"domain_scores_codex":[0.9996593,0.00007155267,0.00005199217,0.0000821565,0.00009276495,0.00004229933],"domain_scores_gemma":[0.999395,0.0001265279,0.00002716125,0.0001271325,0.0002995725,0.00002448113],"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":[0.0008686567,0.0001785438,0.001249376,0.002504375,0.0000542971,0.00350912,0.004276189,0.002293769,0.08162609,0.1792282,0.1884568,0.5357546],"study_design_scores_gemma":[0.0001086979,0.0002094979,0.002325662,0.0002535488,0.0001188476,0.001974023,0.001112301,0.008367653,0.08175719,0.02151418,0.8822072,0.00005130833],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.1331996,0.006677454,0.2588696,0.004248705,0.007437122,0.0006639611,0.02126752,0.01282723,0.5548088],"genre_scores_gemma":[0.5201037,0.006868937,0.2475461,0.001176511,0.0009561722,0.0003459001,0.02602884,0.005139286,0.1918345],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03340487,"threshold_uncertainty_score":0.1117504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00973105556912057,"score_gpt":0.2634557179764159,"score_spread":0.2537246624072953,"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."}}