{"id":"W3202882906","doi":"","title":"METIS-II: the German to English MT system","year":2007,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metis; German; History; Linguistics; Computer science; Archaeology; World Wide Web; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008736964,0.00009326559,0.00008820094,0.00008253993,0.0001932291,0.0001742788,0.001443329,0.00004714393,0.000007454475],"category_scores_gemma":[0.00007531502,0.0000538317,0.00003447204,0.0004882275,0.00001763283,0.0002577821,0.0005484158,0.0001276249,0.00003541472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006126751,"about_ca_system_score_gemma":0.000018297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005410203,"about_ca_topic_score_gemma":0.00002808008,"domain_scores_codex":[0.9990619,0.00002575649,0.0001561062,0.00023804,0.0002573892,0.0002607389],"domain_scores_gemma":[0.9990651,0.00007992287,0.000039148,0.0005920293,0.0001429729,0.00008082628],"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":[0.000002910742,0.00001498349,0.00002776105,0.000020638,0.000007139989,0.00003696456,0.005832496,4.48381e-7,0.00290143,0.9011548,0.01394044,0.07606006],"study_design_scores_gemma":[0.0003546925,0.0003712246,0.0006476867,0.0002621542,0.00002252502,0.0002126866,0.001010571,0.006389004,0.7013858,0.02598271,0.2621714,0.001189598],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004278243,0.0004071869,0.9731621,0.0009821717,0.000377128,0.0001890519,3.169123e-7,0.002104026,0.01849978],"genre_scores_gemma":[0.6324742,3.424026e-7,0.36536,0.001081827,0.0001215062,0.000006221035,1.797793e-7,0.000004883966,0.0009508326],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.875172,"threshold_uncertainty_score":0.2682088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007916549751680718,"score_gpt":0.2734316183348972,"score_spread":0.2655150685832165,"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."}}