{"id":"W2529268278","doi":"","title":"Learning Articulated Skeletons from Motion","year":2007,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Motion (physics); Computer science; Artificial intelligence; Sequence (biology); Probabilistic logic; Feature (linguistics); Motion capture; Series (stratigraphy); Computer vision; Algorithm; Structure from motion","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":[0.0001378031,0.00004268957,0.00003945725,0.00005823465,0.00009467027,0.00006631171,0.00009022724,0.0000313581,0.000315802],"category_scores_gemma":[0.00001568181,0.0000389746,0.00002594439,0.0001515009,0.000006582157,0.000294873,0.00002666901,0.00008618656,0.0007584395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001465039,"about_ca_system_score_gemma":0.000004902407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005864008,"about_ca_topic_score_gemma":0.00002549209,"domain_scores_codex":[0.9995258,0.00001822102,0.0001019753,0.000137043,0.00009449635,0.0001224494],"domain_scores_gemma":[0.9997425,0.00004401155,0.00002826708,0.0001012837,0.00003522843,0.000048686],"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":[0.000003089755,0.00008395493,0.001461801,0.000001543294,0.00001882329,0.00002360483,0.0005312597,0.0002272495,0.03553484,0.01638536,0.000769485,0.944959],"study_design_scores_gemma":[0.001215678,0.0002239435,0.2600939,0.00003861746,0.00002230796,0.00002643446,0.0003523923,0.2585227,0.397522,0.03318798,0.04808599,0.0007080745],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3749972,0.000002804699,0.6130414,0.0001435517,0.00009808551,0.00002145137,8.786994e-8,0.0002195374,0.01147593],"genre_scores_gemma":[0.9862872,0.000001482824,0.01253706,0.0002219233,0.00006464391,7.430999e-7,0.000007929128,0.000002496438,0.0008765427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9442509,"threshold_uncertainty_score":0.9748458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114003695006623,"score_gpt":0.2345753911028907,"score_spread":0.2231750216022284,"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."}}