{"id":"W4408926663","doi":"10.61091/jcmcc125-17","title":"Machine learning and combinatorial analysis-based recognition of sports activity: An investigation using SVM and KNN classifiers","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Artificial intelligence; Pattern recognition (psychology); Machine learning; Computer science","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":[0.001540245,0.000377812,0.0006841049,0.002175195,0.0002344526,0.0009298868,0.0004685571,0.0004128097,0.0007294126],"category_scores_gemma":[0.005125559,0.000127112,0.0004375802,0.001748783,0.0004373951,0.00109286,0.00027922,0.0002854523,0.0002981787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003887554,"about_ca_system_score_gemma":0.0004030802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001425607,"about_ca_topic_score_gemma":0.001232156,"domain_scores_codex":[0.9986408,0.0003707344,0.0001037806,0.0002196319,0.0005866818,0.00007834683],"domain_scores_gemma":[0.9978649,0.0013175,0.0002268529,0.0001061331,0.0004262376,0.0000584202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000770954,0.0006877276,0.1269436,0.0004888795,0.0002606249,0.0002653267,0.0003628425,0.06942234,0.02152809,0.006027828,0.001174101,0.7720677],"study_design_scores_gemma":[0.0000179778,0.0005472373,0.08197065,0.00006475895,0.00006867859,0.0003903625,0.0004119473,0.9030938,0.007916518,0.003834376,0.001640756,0.00004291509],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7086224,0.001881636,0.2800829,0.0002619907,0.0001430894,0.0001748531,0.0003192293,0.0003340362,0.00817992],"genre_scores_gemma":[0.9563683,0.000408093,0.04224904,0.00002606469,0.00002912489,0.00005247627,0.0001712857,0.00001188991,0.0006837479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002175195,"threshold_uncertainty_score":0.00814569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09824008008944138,"score_gpt":0.351062666911981,"score_spread":0.2528225868225396,"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."}}