{"id":"W2133397249","doi":"10.4304/jmm.2.4.45-54","title":"A Three-Dimensional Spatiotemporal Template for Interactive Human Motion Analysis","year":2007,"lang":"en","type":"article","venue":"Journal of Multimedia","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Motion (physics); Human motion; Artificial intelligence; Computer vision; Human–computer interaction; Computer graphics (images)","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":[0.0004021182,0.0004605313,0.0004238829,0.001158098,0.0001675021,0.0009101774,0.0007799081,0.0005232188,0.003653154],"category_scores_gemma":[0.00153206,0.0002237244,0.0006222985,0.001089601,0.0003209393,0.0007488325,0.0006581447,0.0005126819,0.001074297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002880204,"about_ca_system_score_gemma":0.0004533812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001104673,"about_ca_topic_score_gemma":0.0009370971,"domain_scores_codex":[0.9997895,0.00005402988,0.00002242324,0.00003627723,0.00007741696,0.00002034982],"domain_scores_gemma":[0.9995989,0.0001328283,0.00004203091,0.0001038553,0.00008287131,0.0000395213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002998269,0.00008295158,0.001232057,0.0003202364,0.00009065052,0.0003566912,0.0001981758,0.04348131,0.1744798,0.03108378,0.01277977,0.7355947],"study_design_scores_gemma":[0.00002229886,0.0001274064,0.002192582,0.00005662852,0.00005015932,0.0009702293,0.00007169536,0.8904459,0.06957034,0.009810305,0.02661168,0.00007078741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002797088,0.0001257757,0.9950402,0.00004711537,0.00002844662,0.00004685633,0.0002230358,0.001097329,0.0005940596],"genre_scores_gemma":[0.1002176,0.0004467158,0.8963493,0.00008610024,0.00007281597,0.000260279,0.0008133461,0.0003434869,0.001410377],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003653154,"threshold_uncertainty_score":0.01222104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011974660952113,"score_gpt":0.3155014921548135,"score_spread":0.2853817455452924,"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."}}