{"id":"W3133720952","doi":"10.52082/jssm.2021.188","title":"Evaluating Methods for Imputing Missing Data from Longitudinal Monitoring of Athlete Workload","year":2021,"lang":"en","type":"article","venue":"Journal of Sports Science and Medicine","topic":"Sports Performance and Training","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; Alberta Bone and Joint Health Institute; University of Calgary","funders":"Canadian Institutes of Health Research; International Olympic Committee","keywords":"Imputation (statistics); Missing data; Workload; Computer science; Mean squared error; Statistics; Regression; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.008508556,0.0001146052,0.0005592206,0.0002274813,0.0001933295,0.00001944308,0.0001992391,0.0000411073,0.00003887525],"category_scores_gemma":[0.002009963,0.0000806462,0.00004820053,0.0005496427,0.0003744868,0.000404325,0.0001049238,0.0002226968,7.159945e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004297425,"about_ca_system_score_gemma":0.00100054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001288607,"about_ca_topic_score_gemma":2.675152e-7,"domain_scores_codex":[0.9977248,0.000009090885,0.0007919886,0.0002924128,0.000914669,0.0002670165],"domain_scores_gemma":[0.9973375,0.0001926558,0.0007106498,0.0003469388,0.001174619,0.0002376874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009234163,0.00002078062,0.2820298,0.00006394249,0.00003012531,0.0001225288,0.0008010813,0.00000707604,0.1250434,0.00000456518,0.00002588841,0.5917584],"study_design_scores_gemma":[0.003118066,0.0006696885,0.9190516,0.01067125,0.0007908429,0.001454973,0.006189065,0.007195134,0.0496282,0.0004246247,0.0006610767,0.0001454654],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982746,0.01049495,0.004664482,0.0009546007,0.0008750547,0.00008917452,0.000001084088,0.000005297001,0.0001693332],"genre_scores_gemma":[0.7589343,0.0006542605,0.2390991,0.00009708236,0.001180483,3.914394e-7,0.000003424898,0.000007560187,0.0000233752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6370218,"threshold_uncertainty_score":0.3288657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3006473544362758,"score_gpt":0.5298592879686956,"score_spread":0.2292119335324199,"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."}}