{"id":"W2077501275","doi":"10.1177/1938965511434323","title":"A Revenue Management Model for Casino Table Games","year":2012,"lang":"en","type":"article","venue":"Cornell Hospitality Quarterly","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Revenue management; Revenue; Demand forecasting; Operations research; Table (database); Yield management; Intuition; Marketing; Economics; Demand management; Computer science; Business; Engineering; Finance; Data mining","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":[0.001565348,0.001282247,0.001134563,0.0009235801,0.0009040901,0.00329276,0.00324183,0.002325215,0.01200263],"category_scores_gemma":[0.003173242,0.0006467501,0.001053805,0.000993431,0.001118228,0.002879583,0.001409859,0.002531964,0.001634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004104803,"about_ca_system_score_gemma":0.002683418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02471845,"about_ca_topic_score_gemma":0.01375713,"domain_scores_codex":[0.9991714,0.0002333688,0.00003288014,0.0001760767,0.0001211885,0.0002651397],"domain_scores_gemma":[0.999022,0.0004256812,0.0001477573,0.00004299034,0.0001963351,0.0001651154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006162377,0.00007944255,0.000617712,0.00003653878,0.0000142625,0.0001687586,0.00008808328,0.8600421,0.000483191,0.1280208,0.004196737,0.006190693],"study_design_scores_gemma":[0.0000148074,0.00002069372,0.0001398698,0.000008688638,0.00000634065,0.00002858188,0.00003373919,0.9846594,0.00007038874,0.01345755,0.001547023,0.00001295488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1178178,0.0008719176,0.7558435,0.004459982,0.0002385937,0.0004773483,0.002014211,0.0007934662,0.1174832],"genre_scores_gemma":[0.8956182,0.0007303251,0.03228433,0.0002655314,0.0001262095,0.0003495777,0.0006059139,0.0001301157,0.06988993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02471845,"threshold_uncertainty_score":0.0491491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0455246267722787,"score_gpt":0.2288993388329885,"score_spread":0.1833747120607098,"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."}}