{"id":"W622177296","doi":"10.71781/10300","title":"Compression de données d'animation acquises par capture de mouvements","year":2007,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Lossy compression; Motion capture; Animation; Data compression; Cluster analysis; Artificial intelligence; Compression (physics); Motion (physics); Process (computing); Segmentation; Data mining; Computer vision; Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0003832885,0.0005845639,0.0004421104,0.0004401536,0.0082395,0.0002390932,0.001013538,0.0005365218,0.00005583938],"category_scores_gemma":[0.0000803476,0.0006726162,0.0003076527,0.0005207342,0.0002666435,0.001503284,0.0003862593,0.0006591133,0.00006978191],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006872219,"about_ca_system_score_gemma":0.001977991,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0178119,"about_ca_topic_score_gemma":0.002363432,"domain_scores_codex":[0.996507,0.0002312806,0.0005584548,0.000873238,0.0009523773,0.0008776612],"domain_scores_gemma":[0.9976052,0.0001618494,0.0006424838,0.0005502029,0.0004629776,0.0005772699],"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":[0.00185317,0.001422594,0.06083652,0.001195614,0.0006642079,0.008620831,0.2375098,0.05373527,0.3868258,0.08804984,0.006355987,0.1529305],"study_design_scores_gemma":[0.00357989,0.0002761801,0.1505714,0.003521732,0.0005261417,0.002296537,0.053647,0.4400342,0.1652184,0.003033217,0.1749223,0.002373022],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3383382,0.0311538,0.5775043,0.001118151,0.003844137,0.0008778957,0.0000833013,0.0004871695,0.04659306],"genre_scores_gemma":[0.8683925,0.001347557,0.08017579,0.001058187,0.0004088828,0.00002917702,0.0006004845,0.00006727216,0.0479201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5300544,"threshold_uncertainty_score":0.9995725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01141949289603385,"score_gpt":0.2225084774051978,"score_spread":0.211088984509164,"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."}}