{"id":"W2792748241","doi":"10.1080/21642583.2018.1450167","title":"Research on real – time tracking of table tennis ball based on machine learning with low-speed camera","year":2018,"lang":"en","type":"article","venue":"Systems Science & Control Engineering","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shanghai University of Sport; Science and Technology Commission of Shanghai Municipality; St. Francis Xavier University","keywords":"Artificial intelligence; Computer vision; AdaBoost; Computer science; Tennis ball; Ball (mathematics); Classifier (UML); Engineering; Mathematics; sports equipment","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.003383225,0.0001729068,0.0002660916,0.0007769032,0.0004415757,0.0003226381,0.0007833015,0.00005394407,0.000009558225],"category_scores_gemma":[0.0001882619,0.0001370019,0.0000426954,0.002195528,0.0002103268,0.0005278064,0.00005265275,0.0003308832,0.0000705426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001833694,"about_ca_system_score_gemma":0.0002036909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004350276,"about_ca_topic_score_gemma":0.00000715252,"domain_scores_codex":[0.9970064,0.0001352864,0.0003075056,0.0005709184,0.001361516,0.0006183611],"domain_scores_gemma":[0.9984545,0.0002008385,0.000120421,0.0004727865,0.000588845,0.0001626496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005482404,0.00006288953,0.0004884531,0.00005191016,0.00000834321,0.00000887719,0.0002166459,0.6231813,0.3706629,0.004301224,0.00001059722,0.0009520647],"study_design_scores_gemma":[0.0006752557,0.00138362,0.001892261,0.0003411429,0.00000267671,0.000008493098,0.00004029247,0.9889063,0.006467235,0.000002160111,0.0001228279,0.0001577169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4454367,0.0000440148,0.5453364,0.0001646123,0.001079258,0.000816351,0.000005225666,0.0005925596,0.006524966],"genre_scores_gemma":[0.9990702,0.000001002175,0.0005016319,0.00002173331,0.0001129886,0.00001546851,6.180289e-7,0.00001621939,0.0002601101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5536336,"threshold_uncertainty_score":0.5586777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01610692772265505,"score_gpt":0.2776623688820045,"score_spread":0.2615554411593494,"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."}}