{"id":"W3107809658","doi":"10.18280/ria.340518","title":"Recognition of Wrong Sports Movements Based on Deep Neural Network","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Demonstrative; Computer science; Artificial intelligence; Convolutional neural network; Computer vision; Perception; Graph; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000226255,0.0006386817,0.00047057,0.0007985933,0.0001830188,0.0003102293,0.0005003005,0.0004896279,0.0009272723],"category_scores_gemma":[0.0004660273,0.0002408217,0.0004127446,0.0004740348,0.0002383169,0.0003369407,0.000385382,0.0004045973,0.0002700542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003787365,"about_ca_system_score_gemma":0.0003561058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008846694,"about_ca_topic_score_gemma":0.01195192,"domain_scores_codex":[0.9997788,0.00002455176,0.00001416189,0.00007340904,0.00005296463,0.00005620007],"domain_scores_gemma":[0.9998752,0.00003008678,0.00002387394,0.00001576835,0.00003771947,0.00001739631],"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":[0.0005133374,0.0002157517,0.01049183,0.0001128877,0.0001252896,0.0005533798,0.00009435314,0.1226954,0.08640661,0.001312278,0.003430351,0.7740486],"study_design_scores_gemma":[0.000006385965,0.00006368519,0.005796679,0.00001054,0.00001738781,0.00008040763,0.00002161894,0.9856733,0.007131118,0.0006673097,0.0005208952,0.00001067931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.424311,0.001372788,0.5649771,0.0002928155,0.0003053619,0.00008532468,0.0004482224,0.002058357,0.006149039],"genre_scores_gemma":[0.9533787,0.0003921812,0.04196777,0.0001278302,0.00003167352,0.00002867912,0.0004319415,0.00003604853,0.003605176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008846694,"threshold_uncertainty_score":0.0175904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05534893739355826,"score_gpt":0.2562718909518135,"score_spread":0.2009229535582552,"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."}}