{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003184164,0.0004161371,0.0007086212,0.0005870658,0.0001885281,0.0002350388,0.0007285636,0.0004976468,0.0008368628],"category_scores_gemma":[0.0006321415,0.0005052317,0.0004946262,0.001289377,0.0001639894,0.000214126,0.0002527605,0.0005510483,0.00008451047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002325385,"about_ca_system_score_gemma":0.0001302948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003774012,"about_ca_topic_score_gemma":0.0008794581,"domain_scores_codex":[0.9939973,0.003636804,0.0009400384,0.0006406596,0.0004546363,0.0003305064],"domain_scores_gemma":[0.9952627,0.0005610248,0.0006594918,0.001670996,0.001655596,0.0001901683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003330864,0.002111074,0.01193898,0.001342097,0.003827274,0.000007917097,0.04944978,0.2394245,0.05412918,0.4933629,0.002330447,0.1420426],"study_design_scores_gemma":[0.000489541,0.000001162179,0.2030962,0.001952093,0.00152484,0.00000346957,0.0001222131,0.7473019,0.03109802,0.002379407,0.01126953,0.000761628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4486741,0.001525558,0.5060894,0.002392215,0.0002680165,0.0004980214,0.0001545927,0.0003629554,0.0400351],"genre_scores_gemma":[0.9494874,0.001042985,0.04134226,0.00004559149,0.00002977721,0.00002851833,0.001747692,0.00005142768,0.006224303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5078774,"threshold_uncertainty_score":0.9997399,"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."}}