{"id":"W1991522508","doi":"10.1007/s10590-008-9036-3","title":"Semi-supervised model adaptation for statistical machine translation","year":2007,"lang":"en","type":"article","venue":"Machine Translation","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; National Research Council Canada","funders":"","keywords":"Machine translation; Computer science; Translation (biology); Artificial intelligence; Adaptation (eye); Computational linguistics; Natural language processing; Machine learning; Statistical analysis; Statistical model; Statistics; Mathematics; Psychology","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":[0.0008507241,0.0002112749,0.0001870804,0.0002107956,0.0001707331,0.0001070209,0.0003692821,0.0001360633,0.00001097909],"category_scores_gemma":[0.00003757014,0.0001993796,0.00007979352,0.0003311658,0.00002809483,0.0007983192,0.00001338723,0.0001994814,0.000003572101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005074202,"about_ca_system_score_gemma":0.00004974491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007412958,"about_ca_topic_score_gemma":0.0002674785,"domain_scores_codex":[0.9984132,0.00004611783,0.0004370854,0.0004215678,0.000363494,0.0003185368],"domain_scores_gemma":[0.9991304,0.0002926549,0.00009561385,0.0002746722,0.0001153115,0.0000913608],"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":[0.0001250854,0.00005636024,0.00005025417,0.00006846854,0.000009809788,0.000002612065,0.001733263,0.004027517,0.007938693,0.03031659,0.00003388659,0.9556375],"study_design_scores_gemma":[0.0008035268,0.00008408717,0.00008957044,0.00001709532,0.00002168308,0.000005414525,0.000006198932,0.9409888,0.004287011,0.05325133,0.0002175946,0.0002276741],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003810852,0.002643156,0.9940985,0.001029599,0.00009415862,0.0006231944,0.00007293227,0.0007327933,0.0003246097],"genre_scores_gemma":[0.477546,0.000005730005,0.5220373,0.0001176613,0.00003070821,0.00001668983,0.0002136869,0.0000151739,0.00001712657],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9554098,"threshold_uncertainty_score":0.8130466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03837286427808482,"score_gpt":0.3087072662634866,"score_spread":0.2703344019854018,"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."}}