{"id":"W2135482027","doi":"10.1111/cgf.12571","title":"Improving Sampling‐based Motion Control","year":2015,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Human Motion and Animation","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Stylized fact; Sampling (signal processing); Motion (physics); Key (lock); Implementation; Artificial intelligence; Noise (video); Motion control; Computer vision; Sample (material); Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001410105,0.00009897865,0.00009460287,0.0001344629,0.00005077595,0.00006318583,0.00008592031,0.00006016944,0.000007767861],"category_scores_gemma":[0.000007297554,0.000104242,0.00005776413,0.0001208076,0.00001638355,0.0001239739,0.00001137588,0.0001108346,0.0000468207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003055591,"about_ca_system_score_gemma":0.000009963045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003504558,"about_ca_topic_score_gemma":0.000006167337,"domain_scores_codex":[0.9994292,0.00001529165,0.0001453459,0.0001080091,0.0001220392,0.0001801755],"domain_scores_gemma":[0.9996593,0.00001663682,0.00002478691,0.0001330714,0.00006783716,0.00009839378],"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":[0.0000426782,0.0002247973,0.01667251,0.0003871482,0.0001532365,0.00001433982,0.0008056652,0.5399606,0.003283666,0.1710691,0.03555361,0.2318326],"study_design_scores_gemma":[0.0007411292,0.00003939489,0.001848273,0.00001251124,0.000005981231,0.000001301673,0.000007837803,0.9917281,0.0001142364,0.001368997,0.004013143,0.0001191443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02083986,0.00005076987,0.9774643,0.0001208541,0.0006768873,0.000100253,0.000003637741,0.0005016879,0.0002417855],"genre_scores_gemma":[0.9946966,0.000001259528,0.004650615,0.0004313037,0.0001644067,0.000006063756,0.00002275905,0.00002036518,0.000006596415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9738567,"threshold_uncertainty_score":0.4250866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02743252781082081,"score_gpt":0.2244934410426554,"score_spread":0.1970609132318346,"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."}}