{"id":"W4229890538","doi":"10.1145/1399504.1360682","title":"Musculotendon simulation for hand animation","year":2008,"lang":"en","type":"article","venue":"","topic":"Human Motion and Animation","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Animation; Computer science; Pipeline (software); Interactive skeleton-driven simulation; Computer animation; Computer facial animation; Computer graphics (images); Character animation; Skeletal animation; Character (mathematics); Track (disk drive); Motion (physics); Simulation; Computer vision","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002016903,0.0005569925,0.0003951751,0.0003136446,0.000315907,0.0003500433,0.0007533819,0.0006249874,0.008389478],"category_scores_gemma":[0.0009705122,0.0002829123,0.0004132511,0.0002550606,0.0002466699,0.00030337,0.0006594311,0.0006194506,0.0008926241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003748458,"about_ca_system_score_gemma":0.0005561687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002592786,"about_ca_topic_score_gemma":0.003252609,"domain_scores_codex":[0.9998636,0.0000303432,0.000007809385,0.00001732386,0.00007192521,0.000008987217],"domain_scores_gemma":[0.9998165,0.00009334327,0.00001206965,0.00002730506,0.00003407431,0.00001672956],"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":[0.0001245573,0.00005005006,0.0007664849,0.0002161265,0.00005038621,0.0002712845,0.0001172346,0.8629777,0.05339086,0.0163673,0.00323228,0.06243575],"study_design_scores_gemma":[0.00002787493,0.00002328907,0.0001621985,0.00001093439,0.000006840799,0.00007925683,0.00000573351,0.9863091,0.004727148,0.002250301,0.006390263,0.000007017491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02888064,0.0002631421,0.9541836,0.0001964028,0.00009570979,0.0001020882,0.0003292813,0.003737377,0.01221171],"genre_scores_gemma":[0.4245523,0.0006272613,0.5637423,0.0001147919,0.00004120385,0.0004829179,0.0006680898,0.0007324254,0.009038832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008389478,"threshold_uncertainty_score":0.02806556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03406527993012591,"score_gpt":0.2467242849652258,"score_spread":0.2126590050350999,"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."}}