{"id":"W2168737808","doi":"10.1109/icassp.1986.1168806","title":"Plan refinement in a knowledge-based system for automatic speech recognition","year":2005,"lang":"en","type":"article","venue":"","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Speech recognition; Plan (archaeology); Natural language processing; Artificial intelligence; Action (physics); Speaker recognition","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":[0.0005145323,0.0001048136,0.000162141,0.0001890024,0.00004254973,0.00007985832,0.0003004937,0.00005497765,0.00001810921],"category_scores_gemma":[0.00003489917,0.00008984071,0.00005215295,0.0002584889,0.000006677104,0.0001745907,0.00003087272,0.00003928533,0.0004868972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000200005,"about_ca_system_score_gemma":0.0001048508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009443166,"about_ca_topic_score_gemma":0.0007920919,"domain_scores_codex":[0.998972,0.00005545809,0.0003242766,0.00026538,0.0001319777,0.0002509312],"domain_scores_gemma":[0.999379,0.0001252505,0.0000654957,0.0002980354,0.00006156624,0.00007061149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002345702,0.0002901408,0.0003108414,0.0005187831,0.00001165644,0.00001119124,0.0003849399,0.00004199655,0.0006484,0.005251118,0.009679654,0.9828278],"study_design_scores_gemma":[0.003331238,0.0002542087,0.0007786216,0.0004543808,0.000007327969,0.00002848105,0.0001533237,0.942746,0.03878341,0.0004597731,0.01263811,0.0003651366],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1109299,0.000220484,0.8006376,0.001936507,0.001679462,0.002486682,0.00002175079,0.001537933,0.08054973],"genre_scores_gemma":[0.7614878,3.090977e-7,0.2376292,0.0001491236,0.0001882746,0.0002166184,0.0000205512,0.000007652251,0.0003005088],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9824627,"threshold_uncertainty_score":0.6258241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03912540571397419,"score_gpt":0.2602854755763174,"score_spread":0.2211600698623432,"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."}}