{"id":"W7028832650","doi":"","title":"High-performance data-driven control of physically-based human characters","year":2021,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"History of Computing Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Kinematics; Character animation; Motion (physics); Controller (irrigation); Underactuation; Character (mathematics); Motion capture; Task (project management); Animation; Quality (philosophy)","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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0008074685,0.0008428228,0.001368451,0.0006084109,0.0009850567,0.000138166,0.007634957,0.0007278451,0.00002784475],"category_scores_gemma":[0.0004866199,0.0009467108,0.0003030327,0.0009219737,0.0001844302,0.001424771,0.0008025115,0.001716258,0.0001197922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004913043,"about_ca_system_score_gemma":0.0002185586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009493315,"about_ca_topic_score_gemma":0.0001257878,"domain_scores_codex":[0.9944968,0.0003425916,0.001191025,0.001924115,0.001256975,0.0007885264],"domain_scores_gemma":[0.9927179,0.0003104388,0.001454603,0.004642617,0.0006827998,0.0001916626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001463483,0.001037713,0.0000651135,0.001518888,0.0006743396,0.0002159538,0.00002694507,0.001879124,0.3387676,0.3141355,0.00005734103,0.3414752],"study_design_scores_gemma":[0.00737537,0.001631186,0.01444215,0.004801999,0.0007871902,0.00003428734,0.0001175497,0.03704307,0.8605778,0.01431123,0.052586,0.006292198],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992546,0.0001314876,0.0001082449,0.00006709046,0.001931404,0.0005568307,0.0005903062,0.001324579,0.002744006],"genre_scores_gemma":[0.9823647,0.00002512759,0.01478196,0.000227215,0.00005355781,0.00005756711,0.00167744,0.0001167649,0.0006956778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5218102,"threshold_uncertainty_score":0.9992983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.020473291559421,"score_gpt":0.2422256406775882,"score_spread":0.2217523491181672,"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."}}