{"id":"W2042694670","doi":"10.14684/wcca.7.2014.255-259","title":"EDUCAÇÃO MUSICAL: O SOFTWARE MUSIC-AR PARA O ENSINO DE PERCEPÇÃO SONORA PARA CRIANÇAS DA PRÉ-ESCOLA","year":2014,"lang":"pt","type":"article","venue":"Proceedings of World Congress on Communication and Arts","topic":"Diverse Music Education Insights","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Timbre; Musical; Sound (geography); Perception; Software; Computer science; Music education; Psychology; Art; Multimedia; Visual arts; Acoustics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001016953,0.0003928677,0.0002158722,0.0006865332,0.0006683386,0.001813857,0.0006743881,0.0007290078,0.009825379],"category_scores_gemma":[0.003678311,0.0001607043,0.0003378794,0.0004313202,0.001019358,0.001184495,0.001670652,0.0007446592,0.00225953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003762602,"about_ca_system_score_gemma":0.001133506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002662056,"about_ca_topic_score_gemma":0.004626118,"domain_scores_codex":[0.9996094,0.0001214255,0.0000270975,0.00006832193,0.0001375358,0.00003626156],"domain_scores_gemma":[0.9989004,0.0006120225,0.00007813719,0.0001256199,0.0001627727,0.0001210731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002027638,0.0003176326,0.005948245,0.0008619346,0.0000256048,0.0005262286,0.01369201,0.0005867776,0.04657875,0.01917805,0.00753089,0.9045511],"study_design_scores_gemma":[0.0002506138,0.001429741,0.06785812,0.001826188,0.0002630185,0.004516133,0.01477476,0.009867532,0.0388048,0.02142837,0.8388159,0.0001647486],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"software","genre_scores_codex":[0.3581353,0.009370357,0.2953791,0.009729821,0.0007129996,0.0006798236,0.0006572874,0.01694258,0.3083926],"genre_scores_gemma":[0.7847205,0.004368974,0.1567801,0.001265868,0.0002553056,0.0003977272,0.0003052768,0.001016165,0.05089012],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.009825379,"threshold_uncertainty_score":0.03286916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08886344902118398,"score_gpt":0.2914869692108925,"score_spread":0.2026235201897085,"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."}}