{"id":"W2150986509","doi":"10.1109/wmvc.2007.8","title":"Analysis of Irregularities in Human Actions with Volumetric Motion History Images","year":2007,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Down Syndrome Research Foundation; Island Health; University of Victoria","funders":"","keywords":"Smoothness; Computer vision; Artificial intelligence; Motion (physics); Computer science; Standard deviation; Motion analysis; Representation (politics); Orientation (vector space); Visualization; Human motion; Measure (data warehouse); Motion estimation; Motion field; Pattern recognition (psychology); Mathematics; Mathematical analysis; Statistics; Data mining; Geometry","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":[0.0002365261,0.00005432154,0.0001186037,0.001934721,0.00004405295,0.00001751138,0.0001243818,0.00002955496,0.0003268084],"category_scores_gemma":[0.000005876291,0.0000496312,0.00005450414,0.001576363,0.00003949447,0.0004547815,0.00001819989,0.0000620828,0.00000613761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001405566,"about_ca_system_score_gemma":0.00001620052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003585921,"about_ca_topic_score_gemma":0.001120525,"domain_scores_codex":[0.999386,0.0000204595,0.000178041,0.0001516325,0.0001570984,0.0001067597],"domain_scores_gemma":[0.9996098,0.00003410438,0.00007692748,0.0001771179,0.00007437391,0.00002768043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005454808,0.002735401,0.3015288,0.000139376,0.001718074,0.0000913499,0.005513742,0.003050899,0.04819995,0.1511658,0.007914548,0.4778876],"study_design_scores_gemma":[0.000317067,0.00009560745,0.9775332,0.00001097616,0.0001366631,0.000003722686,0.0002782725,0.007647349,0.01218578,0.0003998142,0.001219389,0.000172164],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3114264,0.00003503141,0.6644288,0.00002887726,0.00005509918,0.00004607231,7.200522e-7,0.00004585999,0.02393312],"genre_scores_gemma":[0.9928606,0.000003054681,0.003882394,0.00004308089,0.00001195243,0.000002594912,0.000006419191,0.000002328213,0.003187602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6814342,"threshold_uncertainty_score":0.3578323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02863316553334901,"score_gpt":0.2577189494869855,"score_spread":0.2290857839536365,"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."}}