{"id":"W3083703704","doi":"10.1145/3414685.3417819","title":"Complementary dynamics","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Connaught Fund; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Undo; Computer science; Animation; Rigid body dynamics; Dynamics (music); Motion (physics); Complement (music); Subspace topology; Tracking (education); Space (punctuation); Segmentation; Deformation (meteorology); Rigid body; Computer graphics (images); Computer vision; Classical mechanics; Physics; Artificial intelligence; Acoustics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002224677,0.0000766653,0.00006228545,0.00006286255,0.00008275719,0.0000166974,0.0001109152,0.00003297768,0.0004337524],"category_scores_gemma":[0.000002664186,0.00008649578,0.00005516139,0.0002092211,0.000016362,0.00007414827,0.000001041534,0.0001714082,0.0001013502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002198523,"about_ca_system_score_gemma":0.0000029445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002376552,"about_ca_topic_score_gemma":0.00005550692,"domain_scores_codex":[0.999615,0.000008771907,0.0001098446,0.00008244961,0.00009231336,0.00009162559],"domain_scores_gemma":[0.9997604,0.00001874855,0.000007573306,0.0001307764,0.00001266344,0.00006980295],"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":[0.0001155389,0.0006535624,0.001268928,0.0008831091,0.0009145722,0.0000357394,0.01024735,0.4245615,0.007639279,0.1025781,0.02861786,0.4224845],"study_design_scores_gemma":[0.0008994764,0.0002033113,0.002584886,0.00002731686,0.00006003604,0.000004396011,0.0006571377,0.9576547,0.001999045,0.00497831,0.03047591,0.0004555414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03082955,0.00001585683,0.962307,0.004307259,0.0001889029,0.0001061637,0.00008443847,0.0006677525,0.001493103],"genre_scores_gemma":[0.9970525,0.00007308379,0.001486619,0.001285747,0.00002693482,0.000003735026,0.00004025431,0.00001802906,0.00001310448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9662229,"threshold_uncertainty_score":0.4749284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03104201072107346,"score_gpt":0.2319341338108924,"score_spread":0.200892123089819,"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."}}