{"id":"W39895612","doi":"10.1023/a:1010058117460","title":"Inequalities for Random Utility Models, with Applications to Ranking and Subset Choice Data","year":2000,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Inequality; Ranking (information retrieval); Context (archaeology); Random variable; Statistics; Econometrics; Artificial intelligence; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005078142,0.0001220014,0.0004017696,0.00004996299,0.0001546445,0.00002648039,0.0001694894,0.00008646939,0.00004689638],"category_scores_gemma":[0.00007473275,0.0001306454,0.00001517043,0.0000813206,0.0001380053,0.00009350667,0.0001152148,0.0001078756,0.000005728126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003449624,"about_ca_system_score_gemma":0.00000819273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002094953,"about_ca_topic_score_gemma":0.0001358527,"domain_scores_codex":[0.9985196,0.0001045175,0.0004622388,0.0006966577,0.00001605946,0.0002009067],"domain_scores_gemma":[0.9984059,0.001019217,0.0001058195,0.0004162906,0.000005009972,0.00004777976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008078507,0.0001737752,0.490409,0.0002577962,0.00005813201,1.033891e-7,0.002493426,0.020327,0.00001060859,0.3157294,0.00003187803,0.1697011],"study_design_scores_gemma":[0.002472184,0.00005421876,0.2590322,0.000009646123,0.00001375031,0.000002625691,0.0001248031,0.1223377,0.00001489853,0.6121793,0.003478864,0.0002798604],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5414984,0.0002241312,0.4558386,0.0001542196,0.00001277009,0.000815569,0.00006897275,0.00001578359,0.001371534],"genre_scores_gemma":[0.7677608,0.00002728136,0.2317467,0.0001956491,0.00002744893,0.0001564937,0.00005925624,0.000007691617,0.00001868687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2964499,"threshold_uncertainty_score":0.5327566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4764452096400277,"score_gpt":0.325991379600973,"score_spread":0.1504538300390548,"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."}}