{"id":"W193080678","doi":"","title":"Lessons from NRC's Portage System at WMT 2010","year":2010,"lang":"en","type":"article","venue":"NPARC","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Machine translation; Natural language processing; Translation (biology); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01288075,0.0006693179,0.0007189929,0.001055451,0.002079664,0.004321726,0.004200311,0.003629334,0.007667667],"category_scores_gemma":[0.04554673,0.0007131154,0.0003273985,0.00161777,0.002401457,0.01716929,0.002366465,0.006265106,0.007839127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002736606,"about_ca_system_score_gemma":0.002600073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03753202,"about_ca_topic_score_gemma":0.03968353,"domain_scores_codex":[0.9927117,0.002957132,0.0004550789,0.001075844,0.002404959,0.0003953061],"domain_scores_gemma":[0.9799861,0.008357655,0.0002822988,0.002903358,0.007236794,0.001233793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00037015,0.0004253422,0.005639559,0.0006476514,0.0000330263,0.001474089,0.006471283,0.005324757,0.006497103,0.02999226,0.5123319,0.4307929],"study_design_scores_gemma":[0.000288744,0.000492789,0.006959829,0.0005721123,0.00005597925,0.004024597,0.008183162,0.03792668,0.02739779,0.06958105,0.8441782,0.000339085],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.103163,0.00850462,0.1674557,0.571747,0.003923422,0.000425292,0.003984432,0.01987701,0.1209195],"genre_scores_gemma":[0.5032679,0.007615986,0.3442455,0.03516373,0.002591084,0.0003556825,0.006903833,0.008595681,0.09126054],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03753202,"threshold_uncertainty_score":0.07462716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0119140447714576,"score_gpt":0.2616577710372566,"score_spread":0.249743726265799,"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."}}