{"id":"W4386211560","doi":"10.1109/tensymp55890.2023.10223631","title":"A2D: Anywhere Anytime Drumming","year":2023,"lang":"en","type":"article","venue":"","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Tracking (education); Jazz; Deep learning; Space (punctuation); Track (disk drive); Human–computer interaction; Artificial intelligence; Visual arts; Art; Psychology","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":[0.0004009163,0.001495329,0.0008904054,0.0007223881,0.0004287488,0.001169983,0.00211472,0.001218095,0.04625687],"category_scores_gemma":[0.001647142,0.0003858417,0.0006201669,0.0003239148,0.0003161709,0.001230601,0.003078689,0.0008615055,0.01087213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002438838,"about_ca_system_score_gemma":0.0003488052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001419961,"about_ca_topic_score_gemma":0.00288858,"domain_scores_codex":[0.9996052,0.00004695109,0.00001968987,0.00009975176,0.0001696098,0.00005879256],"domain_scores_gemma":[0.9995479,0.0001333536,0.00001924246,0.0001083985,0.0000886806,0.0001024473],"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":[0.004683083,0.0004852423,0.002740222,0.001644493,0.0003618765,0.001287932,0.0007194036,0.005502422,0.1086737,0.004337632,0.326796,0.542768],"study_design_scores_gemma":[0.001154266,0.002205282,0.01087297,0.0005132403,0.0003233935,0.00493477,0.0005209532,0.3008744,0.0948558,0.01821696,0.5649288,0.0005991455],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04490331,0.004895512,0.6110043,0.0009501567,0.00227991,0.001103512,0.01323806,0.2469409,0.07468435],"genre_scores_gemma":[0.5274436,0.002440986,0.3680393,0.002936495,0.0006807103,0.001793104,0.01673006,0.01092087,0.06901491],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04625687,"threshold_uncertainty_score":0.1547446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0674907380142574,"score_gpt":0.3150278506362216,"score_spread":0.2475371126219642,"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."}}