{"id":"W2739080015","doi":"10.1145/3099564.3106643","title":"Online compression of rigid body simulations using physics-inspired interpolation","year":2017,"lang":"en","type":"article","venue":"","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Interpolation (computer graphics); Pipeline (software); Visualization; Data compression; Window (computing); Dynamical simulation; Computational science; Fluid simulation; Compression (physics); Data visualization; Transmission (telecommunications); Simulation; Computer engineering; Computer graphics (images); Animation; Algorithm; Artificial intelligence; Physics","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":[0.0003765406,0.000478048,0.000514593,0.0005280111,0.0003532528,0.0004763138,0.0008066657,0.0004549673,0.003043429],"category_scores_gemma":[0.002293829,0.0002349615,0.0003473788,0.0007174016,0.0004346154,0.0007600514,0.0009833027,0.0007213905,0.0005531391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003113357,"about_ca_system_score_gemma":0.000606249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001583047,"about_ca_topic_score_gemma":0.001589549,"domain_scores_codex":[0.9997484,0.0000410811,0.00001642349,0.00002505881,0.0001433385,0.00002561925],"domain_scores_gemma":[0.9991755,0.0003723169,0.00006271622,0.0002529227,0.0001020612,0.00003438913],"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":[0.0005097687,0.0002758272,0.002200483,0.000212132,0.00006589166,0.0004691663,0.0003644132,0.7511385,0.0511555,0.01759812,0.004246671,0.1717636],"study_design_scores_gemma":[0.00001399468,0.00003441436,0.0002353202,0.0000067858,0.000003634023,0.00004410917,0.00001757673,0.9824211,0.01320811,0.002425333,0.001580226,0.0000094162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2084397,0.0005653143,0.7750397,0.0004321788,0.0002215439,0.0001578056,0.0004027119,0.00512699,0.009614143],"genre_scores_gemma":[0.7220903,0.0004472962,0.2728028,0.0001325236,0.00006222117,0.0001680188,0.0008118136,0.0007246478,0.002760318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003043429,"threshold_uncertainty_score":0.01018131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06645981542702513,"score_gpt":0.3806260926776673,"score_spread":0.3141662772506422,"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."}}