{"id":"W1539756822","doi":"","title":"Learning mandarin tones at sentence level through training: A pilot study","year":2008,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mandarin Chinese; Sentence; Speech recognition; Psychology; Training (meteorology); Computer science; Linguistics; Natural language processing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002155971,0.0001810027,0.0002384914,0.0001581404,0.0006803578,0.00002269057,0.000339853,0.0001012359,0.001638929],"category_scores_gemma":[0.0001448881,0.0001934487,0.00003235992,0.000255452,0.000310227,0.00003377061,0.00007608894,0.0005622085,0.0007452736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002317175,"about_ca_system_score_gemma":0.0005544611,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1157325,"about_ca_topic_score_gemma":0.09233634,"domain_scores_codex":[0.9981825,0.0001478672,0.0002202852,0.0003986505,0.0002023793,0.0008483411],"domain_scores_gemma":[0.9988774,0.0001412388,0.00005032028,0.0003852081,0.0001045955,0.0004412021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0005804983,0.001396856,0.3807994,0.00003864444,0.0007714558,0.01533362,0.4344207,0.001069995,0.02141525,0.001151018,0.1359177,0.007104816],"study_design_scores_gemma":[0.001235496,0.002582249,0.9726548,0.000008704032,0.00003535368,0.000604881,0.01115164,0.0002619739,0.00002081595,0.0002124984,0.01087508,0.0003564805],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9792805,0.0001430676,0.0004608989,0.0003523846,0.0007204195,0.0003054725,0.00008417245,0.00004631273,0.01860674],"genre_scores_gemma":[0.9637961,0.00003959166,0.0002532984,0.0004215263,0.0001602496,0.0000350702,0.00001782853,0.00003828748,0.03523809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5918553,"threshold_uncertainty_score":0.9992737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2704984481283201,"score_gpt":0.36369937232632,"score_spread":0.09320092419799986,"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."}}