{"id":"W3111050935","doi":"10.3390/app10248793","title":"A Deep Learning and Computer Vision Based Multi-Player Tracker for Squash","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Sports Performance and Training","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Calgary","funders":"","keywords":"Computer vision; Artificial intelligence; Kinematics; Computer science; Inverse kinematics; Squash; Tracking (education); Motion capture; Motion (physics); Robot; Geography","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":[0.0002208627,0.00007491435,0.0001336517,0.00004126125,0.0002034418,0.00003675662,0.00004550298,0.00003275938,0.00003597663],"category_scores_gemma":[0.000007766315,0.00005543797,0.0000238387,0.0001560066,0.0001300128,0.00006774711,0.00001574875,0.00008685666,0.00001201946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000382227,"about_ca_system_score_gemma":0.00002626801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.111483e-7,"about_ca_topic_score_gemma":9.367901e-7,"domain_scores_codex":[0.9993159,0.000001939234,0.000108971,0.0002521251,0.0001508825,0.0001702026],"domain_scores_gemma":[0.9997761,0.00004019692,0.00003671637,0.00003488702,0.00001713365,0.00009495121],"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":[0.0009281418,0.0002913757,0.3643688,0.0005068017,0.00005360869,0.0000367211,0.01208236,0.003029605,0.04037702,0.003353041,0.0007637822,0.5742087],"study_design_scores_gemma":[0.00254409,0.0008884342,0.04960034,0.00003769595,0.00003028611,0.000007278806,0.0009215781,0.935305,0.00266735,0.00001432681,0.007835291,0.0001483007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9369251,0.0000947049,0.05881816,0.001052825,0.00006625556,0.0004428486,4.658911e-7,0.00009581319,0.002503885],"genre_scores_gemma":[0.9621187,0.000003998404,0.03519206,0.002513597,0.0001263835,0.00001665638,0.000005111323,0.000006202702,0.00001730732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9322754,"threshold_uncertainty_score":0.2260695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04224739841895157,"score_gpt":0.3159925588720368,"score_spread":0.2737451604530852,"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."}}