{"id":"W2015655819","doi":"10.1287/inte.1120.0632","title":"Introduction to the Special Issue on Analytics in Sports, Part II: Sports Scheduling Applications","year":2012,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"League; Football; Scheduling (production processes); Analytics; Sport management; Recreation; Operations research; Computer science; Engineering; Advertising; Data science; Business; Political science; Public relations; Operations management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.007576582,0.000308009,0.0004734959,0.001281532,0.0009947052,0.0004622507,0.0008479907,0.0001662841,0.001090768],"category_scores_gemma":[0.001212732,0.0001921808,0.0002320766,0.00325733,0.00009940255,0.0003915353,0.0001266984,0.001152402,0.001623827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002620844,"about_ca_system_score_gemma":0.000187669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002771889,"about_ca_topic_score_gemma":0.00001285297,"domain_scores_codex":[0.994532,0.00003824823,0.001597488,0.0004021412,0.002629297,0.0008008169],"domain_scores_gemma":[0.9969658,0.0003655962,0.0007294117,0.001035557,0.000362824,0.0005407571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002125347,0.0006846769,0.008204288,0.000004027089,0.00007203338,0.000008660581,0.001493867,0.6303902,0.00001402704,0.07056223,0.1204401,0.1679135],"study_design_scores_gemma":[0.0003120078,0.00007418229,0.005534096,0.00002545822,0.0000612679,0.00004839129,0.001498559,0.003072192,0.000112164,0.005778708,0.9832009,0.000282085],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5482351,0.0003098643,0.09144304,0.09449985,0.01831025,0.003379385,0.00008745565,0.0003594564,0.2433756],"genre_scores_gemma":[0.9326882,0.0001090599,0.004485185,0.002208115,0.0558393,0.00003326179,0.00001360826,0.00003246367,0.004590755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8627608,"threshold_uncertainty_score":0.9998224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05280231666078187,"score_gpt":0.3403268695709684,"score_spread":0.2875245529101865,"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."}}