{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005123659,0.00007205392,0.0001237849,0.00009147192,0.00004430525,0.00001864485,0.0002822632,0.00003506929,0.0000049217],"category_scores_gemma":[0.0001580459,0.00006789238,0.00002805571,0.0002140038,0.0000328395,0.0002897955,0.00006355148,0.00009688308,0.000008525989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002506149,"about_ca_system_score_gemma":0.0000148177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001407367,"about_ca_topic_score_gemma":0.00001844281,"domain_scores_codex":[0.9990884,0.00006794557,0.0004534215,0.00009369961,0.0001660401,0.0001305033],"domain_scores_gemma":[0.9993708,0.00009306284,0.0001954601,0.0002831141,0.00004094516,0.00001660231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00000571822,0.0003349436,0.009065674,0.0002672375,0.00004807398,0.000003886228,0.2961133,0.0001871324,0.001344745,0.660406,0.01202861,0.02019469],"study_design_scores_gemma":[0.006008373,0.001718961,0.06129056,0.0005191811,0.00006449285,0.00003878717,0.1273292,0.1824108,0.1274274,0.4335421,0.05722735,0.002422795],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4872017,0.00002483687,0.5097832,0.0004121065,0.0002130042,0.0001498143,0.000006876453,0.00004538411,0.002163118],"genre_scores_gemma":[0.9954853,0.00004308788,0.003987375,0.0003338828,0.000001413369,0.0000121708,0.000001733843,0.000002405688,0.0001326497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5082836,"threshold_uncertainty_score":0.2768571,"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."}}