{"id":"W3209203916","doi":"10.3389/fspor.2021.772181","title":"Storm Clouds on the Horizon: On the Emerging Need to Tighten Selection Policies","year":2021,"lang":"en","type":"article","venue":"Frontiers in Sports and Active Living","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Selection (genetic algorithm); Elite; Countermeasure; Arbitration; Business; Political science; Public relations; Computer science; Law; Engineering; Politics","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":[0.0004478018,0.0001343509,0.0002305237,0.0001633044,0.0002846601,0.00009603373,0.0001059891,0.00004975422,0.0002819636],"category_scores_gemma":[0.00007406635,0.00009597704,0.000057818,0.0003948804,0.00003422209,0.0001028492,0.00004606057,0.000233273,0.000008294061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001004337,"about_ca_system_score_gemma":0.00001665983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002563419,"about_ca_topic_score_gemma":0.00007510615,"domain_scores_codex":[0.9991461,0.00001049065,0.000249722,0.000287077,0.00006288548,0.000243692],"domain_scores_gemma":[0.9995198,0.00006191617,0.0001453436,0.0002015143,0.00002569823,0.00004576838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003299788,0.00008172457,0.869869,0.000008534984,0.00005918793,0.000009949649,0.005389613,0.001029586,0.00001228715,0.09378606,0.02020297,0.009518115],"study_design_scores_gemma":[0.0001010297,0.0001146654,0.8459181,0.0001300959,0.000007646915,0.000003117547,0.005983044,0.00665802,0.0001773187,0.004440734,0.1361807,0.0002856465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9791366,0.0003603384,0.0005759583,0.004685631,0.000852578,0.00013377,0.0000133575,0.00001162761,0.01423007],"genre_scores_gemma":[0.9964746,0.0004468622,0.00009015369,0.001246801,0.0002434539,0.00001532135,0.000002059707,0.00001605094,0.001464664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1159777,"threshold_uncertainty_score":0.391383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01567990995857092,"score_gpt":0.2070895416172929,"score_spread":0.191409631658722,"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."}}