{"id":"W4319990971","doi":"10.18280/ts.390624","title":"Visual Image Recognition of Basketball Turning and Dribbling Based on Feature Extraction","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"AI and Big Data Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Basketball; Computer science; Feature extraction; Convolutional neural network; Computer vision; Feature (linguistics); Pattern recognition (psychology); Optical flow; Grayscale; Frame (networking); Process (computing); Motion (physics); Pixel; Image (mathematics); Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000324891,0.00007938436,0.0000769812,0.0001111009,0.0002361491,0.00005624093,0.0001874007,0.00002054621,0.0001951676],"category_scores_gemma":[0.000007101091,0.00007995446,0.00003090527,0.0002298543,0.00001830705,0.0002684257,0.00008236086,0.0001565843,0.000004600593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003694396,"about_ca_system_score_gemma":0.00002988329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007182675,"about_ca_topic_score_gemma":0.000001348816,"domain_scores_codex":[0.9991389,0.00006872344,0.000137885,0.0002441472,0.0002950403,0.0001152911],"domain_scores_gemma":[0.999594,0.00009400044,0.0001075755,0.0001249514,0.00003859469,0.00004084632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001846041,0.001224176,0.0009366255,0.00008212574,0.00004043108,0.00002248254,0.001115801,0.002250422,0.5549139,0.003046942,0.007481156,0.4287014],"study_design_scores_gemma":[0.001766618,0.001192831,0.01917369,0.00005920116,0.00004373733,0.00003035623,0.0003509257,0.9186854,0.04372298,0.0009833558,0.01355391,0.0004370237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2531757,0.00002644507,0.744069,0.001541448,0.00009211499,0.0002918965,0.00008527271,0.00009086644,0.0006272306],"genre_scores_gemma":[0.9811827,0.000002882294,0.01817167,0.0003569552,0.00004994835,0.00005961046,0.0001514217,0.000005141354,0.00001967381],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9164349,"threshold_uncertainty_score":0.3260448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02198272863740187,"score_gpt":0.2693719782446098,"score_spread":0.2473892496072079,"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."}}