{"id":"W2050636141","doi":"10.5555/1272690.1272709","title":"Kinodynamic skinning using volume-preserving deformations","year":2007,"lang":"en","type":"article","venue":"Symposium on Computer Animation","topic":"Human Motion and Animation","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Skinning; Kinematics; Vector field; Computer science; Deformation (meteorology); Motion capture; Computer vision; Artificial intelligence; Context (archaeology); Geometry; Classical mechanics; Motion (physics); Physics; Mathematics; Geology; Engineering; Mechanical engineering","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":[0.0004579327,0.0005874313,0.0005577224,0.0006086508,0.0003689101,0.001098767,0.0007887877,0.0006485086,0.002555618],"category_scores_gemma":[0.00165822,0.0003929203,0.0005474605,0.0004396972,0.0008325268,0.001370509,0.001405264,0.0008202157,0.0005976201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004087924,"about_ca_system_score_gemma":0.0002996615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007603716,"about_ca_topic_score_gemma":0.000983477,"domain_scores_codex":[0.9996883,0.0000726903,0.0000157138,0.00008076611,0.0001192745,0.00002328294],"domain_scores_gemma":[0.9995527,0.0002022773,0.000055965,0.00009853498,0.00005711481,0.00003339895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001393422,0.00006853026,0.0005022575,0.0001292984,0.00003955941,0.0002842923,0.000483869,0.599967,0.09181562,0.0634828,0.001780415,0.241307],"study_design_scores_gemma":[0.00000850472,0.0000330228,0.0001224329,0.000006949775,0.000004205919,0.0001153172,0.0000269696,0.9657412,0.01388204,0.01561621,0.004424771,0.0000183433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006751053,0.00003564702,0.9915683,0.00002930253,0.00001502135,0.00001955571,0.00001536835,0.0004427368,0.001123089],"genre_scores_gemma":[0.442634,0.0002069973,0.5492011,0.0001040333,0.0000448311,0.0001432841,0.0001468306,0.0009176511,0.006601225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002555618,"threshold_uncertainty_score":0.008549392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01251801972369119,"score_gpt":0.2330003301737306,"score_spread":0.2204823104500395,"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."}}