{"id":"W2145765191","doi":"10.3115/1626355.1626379","title":"NRC's PORTAGE system for WMT 2007","year":2007,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Institute of Standards and Technology","keywords":"Machine translation; Computer science; Phrase; Pruning; Focus (optics); Natural language processing; Artificial intelligence; Feature (linguistics); Task (project management); Translation (biology); Table (database); Speech recognition; Machine translation software usability; Example-based machine translation; Data mining; Engineering; Linguistics","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":[0.001014498,0.00009135524,0.000102243,0.00008968716,0.00007431651,0.0001009739,0.0007173197,0.00006953652,0.000008720294],"category_scores_gemma":[0.00006028172,0.00007072668,0.00004860418,0.0001944736,0.00001401315,0.0003006508,0.0001102239,0.0000700431,0.00002012835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000818333,"about_ca_system_score_gemma":0.00003537152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003091515,"about_ca_topic_score_gemma":0.00001323686,"domain_scores_codex":[0.9990469,0.000006418134,0.0001835486,0.0002471711,0.000232339,0.0002835719],"domain_scores_gemma":[0.9992469,0.00008261733,0.00006563535,0.0003634676,0.0001716197,0.00006977098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008665192,0.00002408631,0.0001131614,0.0001254459,0.000008323776,0.00006717246,0.0001274855,3.194936e-7,0.006980404,0.882875,0.0276895,0.08198047],"study_design_scores_gemma":[0.0005219735,0.0001838061,0.0001859992,0.000138031,0.00001175348,0.0001695095,0.0001598473,0.007985311,0.8997388,0.02627533,0.06394637,0.0006832744],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000221733,0.0006070514,0.9791384,0.0001605162,0.0003266106,0.0001983483,9.237633e-7,0.001736587,0.01760978],"genre_scores_gemma":[0.2654181,5.104037e-7,0.7328165,0.000323719,0.00009294197,0.000008051382,8.946826e-7,0.000006554313,0.001332714],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8927584,"threshold_uncertainty_score":0.288415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01062132966550385,"score_gpt":0.2801154078517293,"score_spread":0.2694940781862254,"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."}}