{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002908645,0.002873829,0.002200184,0.004243386,0.001559689,0.004695938,0.004542537,0.002785652,0.2022354],"category_scores_gemma":[0.01145282,0.001713676,0.001817421,0.004145863,0.0006275164,0.005990883,0.004783748,0.003177939,0.1937666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099405,"about_ca_system_score_gemma":0.002209064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005935539,"about_ca_topic_score_gemma":0.00538325,"domain_scores_codex":[0.9982592,0.0004151816,0.0001944945,0.0003930447,0.0006142988,0.0001237486],"domain_scores_gemma":[0.9971316,0.0005113074,0.0001802949,0.001365277,0.0006245822,0.0001869714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002942539,0.00007814462,0.0002663576,0.0004271478,0.0000810611,0.0003293639,0.00009768693,0.001572856,0.004118667,0.005969567,0.8822293,0.1045355],"study_design_scores_gemma":[0.0003134806,0.00008018926,0.0005813591,0.0001301402,0.00007578277,0.000924104,0.00006516506,0.02169175,0.01323348,0.02084783,0.9418791,0.0001775837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.002666346,0.0008733559,0.1997562,0.001087346,0.001649071,0.0004167945,0.08971962,0.6609702,0.04286101],"genre_scores_gemma":[0.02412784,0.001210714,0.3563376,0.001201755,0.0008037005,0.001629394,0.4405756,0.109562,0.06455149],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2022354,"threshold_uncertainty_score":0.6765447,"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."}}