{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004361239,0.0002734037,0.001046302,0.001007152,0.0004785176,0.000409571,0.0004002051,0.0002817198,0.000005633832],"category_scores_gemma":[0.001954294,0.0002364662,0.0001545665,0.001760963,0.0003435363,0.0003875319,0.0003145965,0.0005888942,1.795724e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006299009,"about_ca_system_score_gemma":0.0001999575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004182028,"about_ca_topic_score_gemma":0.000001939623,"domain_scores_codex":[0.9965942,0.0002834798,0.001448656,0.0004103896,0.001018347,0.0002449166],"domain_scores_gemma":[0.9947317,0.001656387,0.002181459,0.0003458653,0.0009007604,0.00018387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005155077,0.0009941851,0.0467869,0.0003181011,0.0006440826,0.00001571741,0.0009730599,0.0003441866,0.007835835,0.8501645,0.00008973939,0.09131817],"study_design_scores_gemma":[0.003025674,0.0004609664,0.002095058,0.0002583351,0.0006234308,0.00001337998,0.000771771,0.1456159,0.002203619,0.8445164,0.0001650573,0.0002503735],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983793,0.0002150521,0.0127643,0.0002258155,0.0026098,0.0002444807,0.00001126797,0.00003335353,0.0001029315],"genre_scores_gemma":[0.9936246,0.0000657248,0.006066642,0.00001555443,0.0002014972,0.000001615262,0.000006898189,0.00001467271,0.000002824311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1452717,"threshold_uncertainty_score":0.9642813,"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."}}