{"id":"W7035817740","doi":"","title":"Advanced instrumentation and sensor fusion methods in input devices for musical expression","year":2015,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Sport and Mega-Event Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Centre for Interdisciplinary Research in Music Media and Technology; Centre for Research on Brain, Language and Music","keywords":"Instrumentation (computer programming); Sensor fusion; Kalman filter; Signal conditioning; Signal processing; SIGNAL (programming language); Event (particle physics); Digital signal processing; Musical instrument","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.002410865,0.0008357012,0.0006755714,0.0007880534,0.0003599039,0.001988458,0.001104674,0.001490651,0.003352701],"category_scores_gemma":[0.002854584,0.000578068,0.001040565,0.001082491,0.0008621907,0.002010128,0.001243018,0.001628053,0.001320175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008882526,"about_ca_system_score_gemma":0.0006545107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004698911,"about_ca_topic_score_gemma":0.0005479215,"domain_scores_codex":[0.9970624,0.0006593772,0.0001785212,0.0005904736,0.001426911,0.00008229713],"domain_scores_gemma":[0.9990927,0.0003910304,0.0001516434,0.0001321576,0.0002144184,0.00001808723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001778203,0.0001167889,0.001683899,0.001887368,0.0001835771,0.0002026322,0.0006625633,0.04816493,0.1722782,0.09343727,0.003611591,0.6775934],"study_design_scores_gemma":[0.00006490299,0.00119921,0.005557947,0.0009032728,0.000325168,0.000874028,0.0004817011,0.4039693,0.3430801,0.05892305,0.1843776,0.0002437884],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00409723,0.004467524,0.9871351,0.0003322593,0.0001427119,0.0000599004,0.00003508103,0.0002981458,0.003432041],"genre_scores_gemma":[0.1706617,0.0103493,0.8068359,0.00051751,0.0002534628,0.0002805805,0.0001324727,0.000147473,0.01082162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003352701,"threshold_uncertainty_score":0.01274997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04743655131891136,"score_gpt":0.3893847720941866,"score_spread":0.3419482207752753,"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."}}