{"id":"W7099425891","doi":"","title":"Section 7: Speech Understanding A SPEECH UNDERSTANDING SYSTEM BASED UPON A CO-ROUTINE PARSER","year":2008,"lang":"en","type":"article","venue":"","topic":"Linguistics, Language Diversity, and Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Parsing; Scheme (mathematics); Syllable; Grammar; Section (typography); SIGNAL (programming language); Base (topology); Speech processing","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":[0.0005038372,0.0005617994,0.0005066729,0.0004329557,0.0006845891,0.00173691,0.001029124,0.001277443,0.02740088],"category_scores_gemma":[0.0008315559,0.0005585678,0.0003925311,0.0002670046,0.0005682384,0.001355644,0.0006660793,0.0009420346,0.009608481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006184466,"about_ca_system_score_gemma":0.0007539996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007580365,"about_ca_topic_score_gemma":0.005428385,"domain_scores_codex":[0.9997544,0.00004559015,0.00002287172,0.00008989129,0.00005860807,0.00002866552],"domain_scores_gemma":[0.9997169,0.0000915792,0.00001455439,0.00006212546,0.00009180953,0.00002308154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004195439,0.0002255001,0.004059582,0.0007138533,0.00008222347,0.001987181,0.006747016,0.0155948,0.290916,0.1188789,0.07465371,0.4857218],"study_design_scores_gemma":[0.00006581401,0.0003010122,0.004767499,0.0002097931,0.0001161269,0.002503148,0.0008302284,0.1550383,0.2662544,0.02713229,0.5426378,0.0001436562],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01888716,0.0003825069,0.9004177,0.0006115343,0.0002316731,0.0004544186,0.0008165899,0.04280661,0.03539172],"genre_scores_gemma":[0.2557232,0.0007827448,0.6100691,0.0007398296,0.0002697652,0.0005121556,0.002712654,0.003408893,0.1257817],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02740088,"threshold_uncertainty_score":0.09166509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1583598771266066,"score_gpt":0.2515208587946899,"score_spread":0.09316098166808331,"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."}}