{"id":"W4406004996","doi":"10.16891/2317-434x.v12.e3.a2024.pp4486-4497","title":"USE OF ARTIFICIAL NEURAL NETWORKS TO EVALUATE THE INTERACTION OF CONFOUNDING FACTORS WITH DISCRIMINATING FACTORS DURING THE SELECTION OF YOUNG ATHLETES FROM DIFFERENT SPORTS: A PILOT STUDY","year":2025,"lang":"en","type":"article","venue":"Revista Interfaces Saúde Humanas e Tecnologia","topic":"Sports Performance and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Athletes; Confounding; Selection (genetic algorithm); Psychology; Artificial neural network; Applied psychology; Machine learning; Computer science; Physical therapy; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002793472,0.0002682856,0.0006107631,0.0002318504,0.0001960629,0.00006091274,0.0002006927,0.00004410791,0.00005657414],"category_scores_gemma":[0.000157409,0.0001324893,0.00008679394,0.000347899,0.0001799889,0.0001690761,0.000116211,0.0004050416,1.866403e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001297532,"about_ca_system_score_gemma":0.0000241138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009072925,"about_ca_topic_score_gemma":0.0004790851,"domain_scores_codex":[0.9982395,0.0000698314,0.0008120327,0.0003019498,0.0003396181,0.000237131],"domain_scores_gemma":[0.9986366,0.0002489082,0.0005060374,0.0003768304,0.0002023372,0.00002929619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009330715,0.0002550202,0.9725415,0.0001344992,0.0003727317,0.000002046796,0.003332946,0.001979528,0.01866754,0.00009247064,0.000003078687,0.001685639],"study_design_scores_gemma":[0.0003605844,0.002150136,0.9474344,0.001280257,0.000561212,0.000002729924,0.0171724,0.004802164,0.02611044,0.000004783668,0.000004334663,0.0001165514],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982792,0.00009279853,0.0002420232,0.00002433539,0.000218046,0.001045964,0.000004860495,0.00004702713,0.00004571052],"genre_scores_gemma":[0.9998097,0.00001834007,0.0000229744,0.00001102419,0.00003882809,0.00002539829,0.00001354095,0.00001912844,0.00004109487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02510702,"threshold_uncertainty_score":0.5402756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08594123356721252,"score_gpt":0.3339493805562088,"score_spread":0.2480081469889963,"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."}}