{"id":"W3121876819","doi":"","title":"Measuring the Output and Prices of the Lottery Sector: An Application of Implicit Expected Utility Theory","year":2008,"lang":"en","type":"article","venue":"National Bureau of Economic Research","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Lottery; Economics; Index (typography); Econometrics; Parametric statistics; Price index; Yield (engineering); Microeconomics; Marginal utility; Expected utility hypothesis; Metric (unit); Mathematical economics; Mathematics; Statistics; Computer science; Operations management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003280096,0.0005216924,0.0006139773,0.001147034,0.0003438711,0.001845306,0.001269198,0.0005197003,0.002917005],"category_scores_gemma":[0.02017506,0.0002489067,0.000519899,0.002023016,0.001070445,0.002500902,0.001231977,0.0009027051,0.0002860097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002382879,"about_ca_system_score_gemma":0.001359321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01745952,"about_ca_topic_score_gemma":0.0151003,"domain_scores_codex":[0.9983663,0.0008230581,0.00006337727,0.0001898351,0.0004286687,0.000128706],"domain_scores_gemma":[0.9929605,0.004598076,0.001008693,0.0005161026,0.0007559395,0.0001606378],"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":[0.0008676148,0.0006883101,0.2886256,0.0004532368,0.000473339,0.0003503799,0.001659047,0.3815719,0.003809328,0.2040292,0.001605985,0.1158662],"study_design_scores_gemma":[0.00005119396,0.0003766734,0.1195726,0.00006456297,0.00008092513,0.0001216876,0.001035527,0.7438627,0.004288031,0.127874,0.002535973,0.0001361101],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7208182,0.0001613978,0.266507,0.0002337275,0.00001785253,0.0001746296,0.0008995768,0.00008223259,0.01110538],"genre_scores_gemma":[0.9818547,0.00004844215,0.01691615,0.000008506457,0.000004232897,0.00004640244,0.0003497828,0.000009805178,0.0007619356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01745952,"threshold_uncertainty_score":0.03471577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6108723953484728,"score_gpt":0.5283074552787898,"score_spread":0.08256494006968307,"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."}}