{"id":"W2101306983","doi":"","title":"Acoustic and auditory comparisons of polish and taiwanese mandarin sibilants","year":2009,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Music and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Formant; Mandarin Chinese; Acoustics; Speech recognition; Noise (video); Window (computing); Mathematics; Vowel; Physics; Computer science; Linguistics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003568357,0.0003407626,0.0003334526,0.0007039188,0.0003041963,0.0004547446,0.0001971106,0.0001998859,0.002256209],"category_scores_gemma":[0.001257027,0.0001618543,0.0001842018,0.0003109939,0.0004635276,0.0004264222,0.0006198962,0.0001856069,0.0004938057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001190698,"about_ca_system_score_gemma":0.0001872944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001751138,"about_ca_topic_score_gemma":0.002675606,"domain_scores_codex":[0.9998149,0.00002297226,0.00003126042,0.00005270201,0.00004989969,0.00002827599],"domain_scores_gemma":[0.9996729,0.00007611465,0.00004355708,0.00003171944,0.0001211096,0.00005463391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004442438,0.0001655623,0.1002104,0.0004165723,0.0001569706,0.003005583,0.008539898,0.000233075,0.8318316,0.0005651926,0.000210174,0.05022261],"study_design_scores_gemma":[0.00005748722,0.001452175,0.9321423,0.00002203899,0.0001385062,0.003480144,0.007731839,0.0006522527,0.05106184,0.0002121856,0.003016824,0.00003252781],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998871,0.00006727304,0.0002540919,0.000006834641,0.000002909457,0.000005629652,0.00003786155,0.0000052633,0.0007490799],"genre_scores_gemma":[0.9982343,0.0001010641,0.0005654629,0.0000164814,0.000004450468,0.00001741179,0.0001540911,0.00001072115,0.0008960127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002256209,"threshold_uncertainty_score":0.007547796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01037378991018416,"score_gpt":0.2237446915145662,"score_spread":0.2133709016043821,"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."}}