{"id":"W4235709741","doi":"10.32920/ryerson.14653605","title":"Athlete Health Prediction Using Machine Learning Methods","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Machine learning; Random forest; Computer science; Artificial intelligence; Boosting (machine learning); Ensemble learning; Predictive modelling; Task (project management); Class (philosophy); Upsampling; Data mining; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001916866,0.0002522849,0.0004005277,0.0002195698,0.0002097243,0.0004990062,0.001155476,0.0002269278,0.00003756488],"category_scores_gemma":[0.0001729051,0.0002582523,0.0001137722,0.000358225,0.00003076155,0.0004256454,0.002809647,0.001081251,0.000004850845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003825212,"about_ca_system_score_gemma":0.0005649249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006454742,"about_ca_topic_score_gemma":0.00001431656,"domain_scores_codex":[0.9968814,0.000962822,0.0005517206,0.0009697615,0.0003244817,0.0003097995],"domain_scores_gemma":[0.9975933,0.00008924951,0.0005044732,0.001521689,0.0001753576,0.0001159101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006212521,0.0002185212,0.002575634,0.0008250635,0.0001863959,0.00002083617,0.002512739,0.007334259,0.04346752,0.04451734,0.002431674,0.8959038],"study_design_scores_gemma":[0.00006504403,0.00003261042,0.0004612509,0.0001580295,0.00000613572,0.00002362785,0.0000217819,0.9700399,0.01665255,0.001389918,0.01090822,0.0002409561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001897879,0.0007921321,0.9941618,0.001171787,0.0007964522,0.0003100987,0.00002211636,0.001864723,0.0006910472],"genre_scores_gemma":[0.004214746,0.0004576594,0.9939139,0.0005563943,0.0000859521,0.0000376464,0.0003449052,0.00002324338,0.000365579],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9627056,"threshold_uncertainty_score":0.9999869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09394424712436228,"score_gpt":0.3969218102149389,"score_spread":0.3029775630905766,"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."}}