{"id":"W3122097606","doi":"","title":"Selecting the Best? Spillover and Shadows in Elimination Tournaments","year":2014,"lang":"en","type":"article","venue":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Tournament; Spillover effect; Shadow (psychology); Competition (biology); Economics; Shadow price; Econometrics; Microeconomics; Affect (linguistics); Mathematics; Psychology; Mathematical optimization","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":[0.00416096,0.0005103851,0.001419871,0.0008536149,0.001028032,0.002978889,0.0009074822,0.001327005,0.01510535],"category_scores_gemma":[0.01407778,0.0005420155,0.0009049252,0.0006195773,0.001783917,0.001914411,0.001960943,0.001542083,0.0008502335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001112092,"about_ca_system_score_gemma":0.0007030402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005695075,"about_ca_topic_score_gemma":0.009699431,"domain_scores_codex":[0.9984986,0.0004743143,0.00006499002,0.0003508653,0.0001635727,0.0004477683],"domain_scores_gemma":[0.9853998,0.008897867,0.002938879,0.0009985982,0.0003831526,0.001381666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002507679,0.001827681,0.5806091,0.0003331791,0.0006855994,0.001877994,0.002924116,0.19002,0.007143142,0.115738,0.005130233,0.09120335],"study_design_scores_gemma":[0.0003720001,0.001485888,0.4448683,0.0001396775,0.0006496403,0.0008358859,0.002575304,0.3649279,0.002516248,0.175674,0.005720669,0.0002344243],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.979412,0.0003311712,0.01114917,0.0004446263,0.00001870519,0.00005293264,0.0001382938,0.00003779712,0.008415269],"genre_scores_gemma":[0.9975299,0.00008960571,0.0004865261,0.00004475581,0.0000182037,0.00001091708,0.00004975428,0.000004782803,0.001765582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01510535,"threshold_uncertainty_score":0.05053246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03210508230517647,"score_gpt":0.2249752601537191,"score_spread":0.1928701778485426,"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."}}