{"id":"W2949008176","doi":"","title":"Demonstration of the German to English METIS-II MT system.","year":2007,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Metis; German; Computer science; History; World Wide Web; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007732158,0.0005169612,0.0003616547,0.0003902445,0.0007468213,0.001029537,0.0007092691,0.0008868217,0.02593807],"category_scores_gemma":[0.001858355,0.0002413237,0.0001947866,0.0004808531,0.0003287898,0.001206656,0.000999916,0.0006646946,0.01112151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003433543,"about_ca_system_score_gemma":0.0005978987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009794752,"about_ca_topic_score_gemma":0.01377621,"domain_scores_codex":[0.9996488,0.0001099582,0.00003168506,0.00006380507,0.0001000608,0.00004558586],"domain_scores_gemma":[0.9992148,0.0002766919,0.00002327252,0.0001227947,0.000250159,0.0001122948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002952088,0.0007071757,0.005852341,0.001599807,0.0001904488,0.00828677,0.003139113,0.004907005,0.1788335,0.02992741,0.4533035,0.3103009],"study_design_scores_gemma":[0.001536612,0.001367837,0.02828102,0.0003299303,0.0002192136,0.00910804,0.003276203,0.1235737,0.2349381,0.01902014,0.5780875,0.0002616506],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.366259,0.002133747,0.2098992,0.009141765,0.002908373,0.001424184,0.03477233,0.1075382,0.2659231],"genre_scores_gemma":[0.799487,0.0004911806,0.1280864,0.0008838684,0.0002042899,0.0004064623,0.01627057,0.001598997,0.05257114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02593807,"threshold_uncertainty_score":0.08677149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00698526190228118,"score_gpt":0.2635432346271535,"score_spread":0.2565579727248723,"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."}}