{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00306365,0.0007089725,0.0005577345,0.0004690659,0.0002409257,0.0003433745,0.0002965471,0.000401071,0.0006481165],"category_scores_gemma":[0.005495275,0.0002117848,0.000554543,0.0003109945,0.0002325132,0.0002672376,0.0003486164,0.0003251855,0.00009922556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003321692,"about_ca_system_score_gemma":0.0006142691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004901325,"about_ca_topic_score_gemma":0.003334087,"domain_scores_codex":[0.9990344,0.0006253427,0.00005398428,0.0001223307,0.00009112825,0.00007294161],"domain_scores_gemma":[0.9970081,0.001884099,0.0003150842,0.0001595983,0.0004256725,0.0002073801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003976197,0.003112764,0.9042287,0.0001083876,0.0004692658,0.000163241,0.0003580528,0.006794056,0.006350425,0.00008463188,0.0001120697,0.0742421],"study_design_scores_gemma":[0.0002651589,0.01604574,0.8649704,0.0000435368,0.0005003585,0.0002906483,0.0005604448,0.1139282,0.002800137,0.000217909,0.0003364862,0.00004105168],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973028,0.00006191814,0.002340738,0.00001703736,0.000004799293,0.00006672433,0.00003935178,0.000006660313,0.0001600385],"genre_scores_gemma":[0.994276,0.00007541499,0.005153753,0.00001862418,0.000009552801,0.0001561997,0.0001226725,0.000002866447,0.0001848948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004901325,"threshold_uncertainty_score":0.01620233,"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."}}