{"id":"W2617367655","doi":"","title":"Analysis of Timpani Preparatory Gesture Parameterization","year":2009,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Human Motion and Animation","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"","keywords":"Gesture; Computer science; Animation; Character animation; Motion capture; Gesture recognition; Motion (physics); Character (mathematics); Relevance (law); Computer animation; Virtual reality; Interaction technique; Artificial intelligence; Computer vision; Human–computer interaction; Computer graphics (images); Mathematics","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":[0.000290014,0.0006020898,0.0004169915,0.001120421,0.0003998781,0.000792802,0.0004642374,0.0005034548,0.0123269],"category_scores_gemma":[0.002018297,0.0002547622,0.0004038179,0.001045238,0.0002620179,0.0004504929,0.000439644,0.0005792988,0.001946023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003211097,"about_ca_system_score_gemma":0.0004084308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001443996,"about_ca_topic_score_gemma":0.001685718,"domain_scores_codex":[0.999733,0.00003264799,0.00001332254,0.00006174549,0.0001120323,0.00004717267],"domain_scores_gemma":[0.9992284,0.000349685,0.00006500514,0.0001583137,0.0001616265,0.00003690518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001512141,0.0001074109,0.005368847,0.0003455584,0.00004854697,0.0005362287,0.000309462,0.03707338,0.4311245,0.004964706,0.002082578,0.5165266],"study_design_scores_gemma":[0.0000353217,0.0003199971,0.06272712,0.00007204327,0.00008516706,0.001070954,0.0003107899,0.7078344,0.2129335,0.002774044,0.01175147,0.00008517267],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4420031,0.001009373,0.532492,0.0002491463,0.0001063147,0.0001522873,0.001079725,0.003174863,0.0197331],"genre_scores_gemma":[0.9484208,0.000256126,0.04217432,0.00002755414,0.00002141746,0.00008272602,0.0009615726,0.0005042443,0.00755134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0123269,"threshold_uncertainty_score":0.04123759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01170079952186352,"score_gpt":0.2224723052531289,"score_spread":0.2107715057312654,"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."}}