{"id":"W1963819142","doi":"10.1385/ni:1:3:239","title":"Event Identification in Movement Recordings by Means of Qualitative Patterns","year":2003,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"","keywords":"Matching (statistics); Sensitivity (control systems); Acceleration; Pattern recognition (psychology); Event (particle physics); Computer science; Pattern matching; Movement (music); Artificial intelligence; Qualitative analysis; Identification (biology); Infinitesimal; Algorithm; Mathematics; Qualitative research; Statistics; Engineering; Physics; Electronic engineering; Mathematical analysis; Biology","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":[0.000611509,0.0003397726,0.0002015938,0.001627241,0.0003102166,0.0008315103,0.0002861469,0.0004722434,0.003665223],"category_scores_gemma":[0.003757326,0.0001702686,0.0001856057,0.001351775,0.000527155,0.0008371151,0.000602873,0.00047047,0.0007829555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000190621,"about_ca_system_score_gemma":0.0001977225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006202378,"about_ca_topic_score_gemma":0.0008702219,"domain_scores_codex":[0.9997292,0.00006795146,0.00001840308,0.00006236474,0.00008359233,0.00003853858],"domain_scores_gemma":[0.9985455,0.0009261212,0.0001331835,0.0001209351,0.0001832734,0.0000909165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002028423,0.00007666809,0.00942585,0.0006512824,0.0000545325,0.0004835283,0.00181121,0.003368167,0.5696349,0.007182916,0.001296333,0.4039863],"study_design_scores_gemma":[0.0003782015,0.001208415,0.3007379,0.000508654,0.0003152329,0.004617048,0.004640321,0.248308,0.3678062,0.05289582,0.01834474,0.0002394518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2309268,0.0006313366,0.756713,0.0002272035,0.0001071043,0.0004373422,0.001749566,0.001411576,0.007796059],"genre_scores_gemma":[0.8593858,0.0003729729,0.1376012,0.00004469857,0.00005678318,0.000227828,0.0004900699,0.000145059,0.001675485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003665223,"threshold_uncertainty_score":0.01226133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02900613327567828,"score_gpt":0.2978764365553753,"score_spread":0.268870303279697,"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."}}