{"id":"W2894766094","doi":"10.1145/3272127.3275014","title":"SFV","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":201,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Motion capture; Character animation; Animation; Reinforcement learning; Artificial intelligence; Leverage (statistics); Computer animation; Motion (physics); Physics engine; Computer vision; Human–computer interaction; Computer graphics (images)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003941743,0.00006474271,0.00004550998,0.0001271801,0.0001157499,0.00001781521,0.00009033168,0.00004982824,0.0005685957],"category_scores_gemma":[0.000004106129,0.00006765542,0.00004410035,0.0002114475,0.0000419066,0.00007669587,4.998803e-7,0.0001168599,0.0004257207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001159091,"about_ca_system_score_gemma":0.000003092092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000015106,"about_ca_topic_score_gemma":0.00003833113,"domain_scores_codex":[0.9996656,0.000007111771,0.00008228225,0.00007086156,0.00007914884,0.00009500969],"domain_scores_gemma":[0.9996982,0.0000163883,0.000005904488,0.0002123801,0.00002840792,0.00003877857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009482648,0.0009465343,0.0005127564,0.0003573605,0.0006764733,0.00001574182,0.007864912,0.0277086,0.04470846,0.07542951,0.04054615,0.8011387],"study_design_scores_gemma":[0.003033223,0.001282069,0.0298612,0.0002470634,0.000201519,0.00005065016,0.0005630767,0.197868,0.120922,0.06873544,0.5750685,0.002167246],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1337645,0.00002603758,0.8510765,0.0004496806,0.0008677621,0.0001088094,0.00001425167,0.001065183,0.01262729],"genre_scores_gemma":[0.9985536,0.00005991726,0.0009091935,0.0002118573,0.00006770824,0.000006725106,0.000002456856,0.00001546861,0.0001730764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8647891,"threshold_uncertainty_score":0.6225724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02061558315292415,"score_gpt":0.2363071741584292,"score_spread":0.215691591005505,"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."}}