{"id":"W2525799065","doi":"","title":"MULTI-DIMENSIONAL SYNCHRONIZATION FOR RHYTHMIC SONIFICATION","year":2012,"lang":"en","type":"article","venue":"","topic":"Plant and Biological Electrophysiology Studies","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Sonification; Synchronizing; Synchronization (alternating current); Rhythm; Computer science; Motion (physics); Gait; Exploit; Motion capture; Process (computing); Artificial intelligence; Real-time computing; Computer vision; Simulation; Acoustics; Human–computer interaction; Telecommunications; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005182765,0.0004978509,0.0003687571,0.0004823081,0.0002219742,0.0005261573,0.000520721,0.000585268,0.003701858],"category_scores_gemma":[0.002303068,0.0002365807,0.0004238858,0.0006617448,0.0006454484,0.0006086294,0.00073576,0.0006140613,0.001316982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002715787,"about_ca_system_score_gemma":0.0002340537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003883116,"about_ca_topic_score_gemma":0.0005071819,"domain_scores_codex":[0.9996712,0.0001102515,0.00001988856,0.00007804923,0.0001059022,0.00001461386],"domain_scores_gemma":[0.9993931,0.0002882851,0.00007260656,0.0001447699,0.0000773437,0.00002394639],"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":[0.0002835456,0.00005515213,0.001193394,0.0003526426,0.00008156431,0.0002561054,0.0002481353,0.1165909,0.2115071,0.08982973,0.004163073,0.5754386],"study_design_scores_gemma":[0.00003550993,0.0001481094,0.001856683,0.00005394865,0.0000416625,0.0005334511,0.00004965492,0.895414,0.04963661,0.02944574,0.02272998,0.00005472907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003448865,0.0002040871,0.9950001,0.00005465268,0.0000392106,0.00002125126,0.00003570005,0.0002149657,0.0009811947],"genre_scores_gemma":[0.1074654,0.0005327183,0.8887103,0.00007442722,0.0001479813,0.0001603945,0.0002062403,0.0002139728,0.002488472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003701858,"threshold_uncertainty_score":0.012384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03669384031846457,"score_gpt":0.2357996692670469,"score_spread":0.1991058289485824,"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."}}