{"id":"W2147267580","doi":"10.1109/tmm.2010.2089786","title":"Training Surrogate Sensors in Musical Gesture Acquisition Systems","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Music and Audio Processing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Gesture; Computer science; Microphone; SIGNAL (programming language); Speech recognition; Gesture recognition; Data acquisition; Artificial intelligence; Musical instrument; Human–computer interaction; Signal processing; Computer vision; Computer hardware; Digital signal processing; Acoustics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0003080636,0.0001738787,0.0002008266,0.0002224575,0.0001637814,0.0001497554,0.0003304697,0.0001806428,0.00004731285],"category_scores_gemma":[0.0000095138,0.0001630658,0.0000720534,0.0004129922,0.00006886769,0.0004448559,0.000001918178,0.0007111219,0.00009760189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003292376,"about_ca_system_score_gemma":0.00007647744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005480714,"about_ca_topic_score_gemma":0.00007996906,"domain_scores_codex":[0.9986324,0.00006802284,0.0002583011,0.0004154468,0.0002872654,0.000338615],"domain_scores_gemma":[0.9992111,0.0001883221,0.00006691984,0.0003417737,0.00005217193,0.0001397457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004568424,0.0005611886,0.00008887664,0.00009195371,0.00004169749,0.000204316,0.02485757,0.0551744,0.1122387,0.0005323692,0.0002951554,0.8058681],"study_design_scores_gemma":[0.001398195,0.0000747538,0.002535689,0.0001633899,0.00001630911,0.0001114372,0.0003910877,0.9761475,0.016813,0.0001160345,0.001714631,0.0005180095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3444043,0.00001189845,0.6511492,0.0005537934,0.003068262,0.0001628157,0.000006030349,0.0002134987,0.0004302362],"genre_scores_gemma":[0.9833815,0.000004463958,0.01596899,0.000263046,0.0001781018,0.00003232741,0.000001493021,0.00001474129,0.0001553664],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9209731,"threshold_uncertainty_score":0.6649631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02475520679632822,"score_gpt":0.2567921720099744,"score_spread":0.2320369652136462,"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."}}