{"id":"W631043532","doi":"10.1515/jqas-2013-0070","title":"Realignment in the NHL, MLB, NFL, and NBA","year":2014,"lang":"en","type":"article","venue":"Journal of Quantitative Analysis in Sports","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"League; Atlanta; Variety (cybernetics); Computer science; Operations research; Marketing; Business; Geography; Engineering; Artificial intelligence; Metropolitan area","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002218812,0.0003962725,0.0004383234,0.001011952,0.0005215851,0.00101201,0.001027422,0.0005993853,0.004116121],"category_scores_gemma":[0.006438731,0.0002651198,0.0004563133,0.0006225434,0.0006158829,0.0006854936,0.000891272,0.0006188584,0.0003018052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00329386,"about_ca_system_score_gemma":0.001798451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03087174,"about_ca_topic_score_gemma":0.04353858,"domain_scores_codex":[0.9990404,0.0004489573,0.0000281921,0.0001783725,0.0001173742,0.0001865846],"domain_scores_gemma":[0.9976689,0.001187101,0.0003613622,0.0002003009,0.0002734263,0.0003089724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006720711,0.0006752093,0.0283314,0.0001734291,0.00005547961,0.0001244329,0.0004930529,0.7492217,0.004059425,0.02714768,0.004506006,0.1845401],"study_design_scores_gemma":[0.00007012832,0.0003612974,0.006863626,0.00002592613,0.00001935492,0.00003092866,0.0004510816,0.9760658,0.001820713,0.009538748,0.004735782,0.00001671088],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8647011,0.0002136894,0.1166548,0.0005323843,0.00004515566,0.0003293108,0.0004221064,0.0003245483,0.01677696],"genre_scores_gemma":[0.9003805,0.00005206481,0.09469312,0.00003805759,0.000008686741,0.0001514422,0.0003428682,0.00004255107,0.004290754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03087174,"threshold_uncertainty_score":0.06138408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1307740006839047,"score_gpt":0.4374553863436828,"score_spread":0.306681385659778,"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."}}