{"id":"W2757804460","doi":"10.3968/9725","title":"On the Analysis of the Style and Feature of Wang Zhixin’s Vocal Music Composition","year":2017,"lang":"en","type":"article","venue":"Cross-cultural communication","topic":"Diverse Musicological Studies","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Singing; Composition (language); Style (visual arts); Vocal music; Opera; Art; Feature (linguistics); Musical; Speech recognition; Musical composition; Variety (cybernetics); Literature; Linguistics; Music; Visual arts; Computer science; Acoustics; Music education; Philosophy; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002591051,0.0001546359,0.00009139781,0.001900386,0.0006672954,0.0007499155,0.0001180802,0.0001493583,0.001961822],"category_scores_gemma":[0.000797469,0.00004270192,0.000104206,0.001554445,0.0004363011,0.0004309077,0.0004063297,0.0001659378,0.0001597787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002717227,"about_ca_system_score_gemma":0.0001661143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001436018,"about_ca_topic_score_gemma":0.003219874,"domain_scores_codex":[0.9998135,0.00003843252,0.00001408922,0.00003361344,0.00007471841,0.00002566218],"domain_scores_gemma":[0.9996419,0.0001219246,0.00007510183,0.00001731272,0.00008659564,0.00005709982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005430264,0.00007702791,0.3486893,0.0005475992,0.0001187758,0.0044013,0.1085119,0.0007572971,0.06462787,0.01698996,0.004188634,0.4505473],"study_design_scores_gemma":[0.000009586567,0.0001119528,0.9259304,0.00007245135,0.00006381577,0.001378134,0.03830627,0.002238254,0.00414832,0.001469973,0.02623335,0.0000375615],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9792203,0.0003687356,0.001376336,0.0001146439,0.00003546862,0.00001418222,0.00008693647,0.00001298637,0.01877039],"genre_scores_gemma":[0.9953075,0.0002561868,0.0005491456,0.00002234065,0.00002732698,0.000007752669,0.0001026173,0.000006479019,0.003720692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001961822,"threshold_uncertainty_score":0.006562948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1023787262804862,"score_gpt":0.3047614715981568,"score_spread":0.2023827453176706,"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."}}