{"id":"W4414702733","doi":"10.1186/s40798-025-00903-z","title":"Predicting Future Performance in Powerlifting: A Machine Learning Approach","year":2025,"lang":"en","type":"article","venue":"Sports Medicine - Open","topic":"Sports Performance and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Lift (data mining); Normative; Athletes; Training set; Training (meteorology); Support vector machine","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":[0.001540502,0.0003040812,0.0007801903,0.0005162662,0.0002099775,0.0000284427,0.0003439962,0.0001562105,0.0004901015],"category_scores_gemma":[0.0000768204,0.0002324632,0.00005801724,0.0009318011,0.0001155655,0.0003428684,0.0002201699,0.001035166,0.000008294344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009439397,"about_ca_system_score_gemma":0.0002713231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003495011,"about_ca_topic_score_gemma":0.00002723105,"domain_scores_codex":[0.9976918,0.00001520575,0.0007305712,0.0005558528,0.000480759,0.0005258796],"domain_scores_gemma":[0.9990848,0.00002191963,0.0002077445,0.0004406403,0.00008744946,0.0001574791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003179437,0.0001144632,0.9624682,0.000284692,0.00003838719,0.0001301771,0.002286876,0.0001154022,0.00002763752,0.0002366609,0.0003102285,0.03366934],"study_design_scores_gemma":[0.004566514,0.0002929359,0.9155459,0.002308364,0.0001125026,0.0001632541,0.004557177,0.009494375,0.00004409645,0.00002175106,0.06268349,0.0002096742],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7388217,0.001158753,0.00002471148,0.002033307,0.0004933857,0.0008619402,6.334715e-7,0.000113155,0.2564924],"genre_scores_gemma":[0.9864818,0.0005233707,0.0009389433,0.00149257,0.0006364262,0.00006429554,0.0001887312,0.00003760627,0.009636221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2476602,"threshold_uncertainty_score":0.9479573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01680442105021517,"score_gpt":0.2945511684808464,"score_spread":0.2777467474306312,"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."}}