{"id":"W1526377340","doi":"10.1007/978-3-540-76856-2_78","title":"Video Segmentation for Markerless Motion Capture in Unconstrained Environments","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Segmentation; Computer vision; Computer science; Motion capture; Cluster analysis; Process (computing); Context (archaeology); Motion (physics); Tracking (education); Parametric statistics; Geography; Mathematics","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":[0.0002097005,0.0007582096,0.0006652587,0.0008283897,0.0003585732,0.0008317612,0.0009497554,0.0007898223,0.004587338],"category_scores_gemma":[0.0008849393,0.0006326181,0.000363061,0.0009062181,0.0002746319,0.0008149298,0.0006350738,0.0005408829,0.001786286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004250035,"about_ca_system_score_gemma":0.0004812681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002979506,"about_ca_topic_score_gemma":0.005038611,"domain_scores_codex":[0.9997539,0.0000288085,0.000009305838,0.00007681557,0.00009578935,0.00003539454],"domain_scores_gemma":[0.9997419,0.00009633217,0.00002394748,0.00005418251,0.00006553134,0.00001807737],"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":[0.0003931861,0.00007041045,0.0004119982,0.0002429551,0.00004458351,0.0001565071,0.0001305679,0.02718445,0.3214551,0.004959757,0.006483173,0.6384674],"study_design_scores_gemma":[0.00002860195,0.0001415981,0.002433036,0.00006323538,0.00003832068,0.0006542828,0.00007168085,0.7321715,0.2412341,0.005945145,0.01717444,0.000043983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008217584,0.0003887549,0.9876614,0.00003177868,0.00004181383,0.00004443882,0.0001890069,0.001532587,0.001892713],"genre_scores_gemma":[0.1507608,0.0009285386,0.8388448,0.00009526881,0.00006553246,0.0001013366,0.00110747,0.000650548,0.007445714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004587338,"threshold_uncertainty_score":0.01534623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01990176701086773,"score_gpt":0.2780898151969188,"score_spread":0.2581880481860511,"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."}}