{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001040493,0.0001110939,0.0001776817,0.0001373408,0.0001520993,0.0001169191,0.0002437829,0.00007070592,0.000006984101],"category_scores_gemma":[0.00006120391,0.0001123297,0.00001351114,0.0001544779,0.0001179925,0.0001552069,0.00004485464,0.0001162382,0.000001950066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004921084,"about_ca_system_score_gemma":0.0004229273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001703328,"about_ca_topic_score_gemma":0.002169199,"domain_scores_codex":[0.9991844,0.00001421021,0.0001583525,0.0002232559,0.0001227565,0.0002969781],"domain_scores_gemma":[0.9991977,0.00003895094,0.00006907272,0.0002221171,0.00006803938,0.0004041132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00002414554,0.0001777412,0.01668604,0.0007125348,0.00009167055,0.0007516174,0.007625182,0.003444196,0.106677,0.01833502,0.4679961,0.3774787],"study_design_scores_gemma":[0.001343239,0.0003423249,0.7145454,0.0003354028,0.0001177872,0.0002882926,0.0005621325,0.2618491,0.0004929548,0.007002505,0.01202663,0.00109419],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5713459,0.001334657,0.4173076,0.002859559,0.0008154185,0.0002285555,0.00007700994,0.00009985093,0.005931479],"genre_scores_gemma":[0.9905476,0.00003010521,0.007681025,0.001452137,0.0001174563,6.006479e-7,0.000001720255,0.000005131071,0.0001642299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6978594,"threshold_uncertainty_score":0.4580671,"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."}}