{"id":"W4407776479","doi":"10.1016/j.eswa.2025.126821","title":"Bio-inspired algorithms for the characterization of excellent performance in handball players: A data-driven methodology","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; European Regional Development Fund; European Commission; Emissions Reduction Alberta","keywords":"Computer science; Characterization (materials science); Machine learning; Algorithm; Artificial intelligence; Nanotechnology","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":[0.0005676922,0.0001085461,0.0003269707,0.0001917339,0.0001284557,0.00003306989,0.0004392362,0.00006648331,0.00001377659],"category_scores_gemma":[0.00001270042,0.0000848437,0.00003187915,0.0003860847,0.00005942741,0.0001308248,0.0000554529,0.00006193711,0.000009528965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004648421,"about_ca_system_score_gemma":0.00004216998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004328858,"about_ca_topic_score_gemma":0.00005492385,"domain_scores_codex":[0.9988631,0.0000100247,0.0005927039,0.0003387919,0.00003235302,0.0001630247],"domain_scores_gemma":[0.9987013,0.0001121105,0.0003620939,0.0007440639,0.00005719666,0.00002319465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003784024,0.0009355874,0.2668985,0.001305053,0.0009109334,0.000001188635,0.005673458,0.02315549,0.00263494,0.6649579,0.002657722,0.03049083],"study_design_scores_gemma":[0.000557687,0.00004294725,0.02269848,0.00006717591,0.00001103377,0.000001456545,0.0002163035,0.610468,0.0001588518,0.00007169729,0.3655711,0.0001353027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02352858,0.003086802,0.9692096,0.0007456268,0.000294618,0.001874646,0.0004340911,0.00002276049,0.0008032542],"genre_scores_gemma":[0.9917447,0.001495429,0.003074459,0.0001256225,0.0001232815,0.002121804,0.0003213487,0.0000156307,0.0009777541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9682161,"threshold_uncertainty_score":0.3459826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.11928865167313,"score_gpt":0.3057551225736715,"score_spread":0.1864664709005416,"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."}}