{"id":"W2909061514","doi":"10.1287/deca.2021.0439","title":"A Characterization of Lexicographic Preferences","year":2021,"lang":"en","type":"article","venue":"Decision Analysis","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Lexicographical order; Characterization (materials science); Preference; Mathematical economics; Preference relation; Contrast (vision); Set (abstract data type); Mathematics; Key (lock); Order (exchange); Computer science; Economics; Combinatorics; Artificial intelligence; Statistics","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":[0.0001668951,0.00006141338,0.000208572,0.0003589744,0.00004321473,0.00009326765,0.0003733553,0.00003730998,0.0001516544],"category_scores_gemma":[0.00005780315,0.00004759034,0.0002086487,0.004200967,0.00001677194,0.0001786041,0.0001242165,0.00003576851,0.0000127089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004281712,"about_ca_system_score_gemma":0.00002929284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007474403,"about_ca_topic_score_gemma":0.00003328747,"domain_scores_codex":[0.9990463,0.00005450522,0.0002440634,0.000262726,0.0003034499,0.00008894222],"domain_scores_gemma":[0.9990882,0.00007696929,0.0001091261,0.0005057668,0.0001769522,0.00004296865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000009257642,0.0002547769,0.04307927,0.00000777949,0.000659633,0.0000317788,0.0005263438,0.0003755504,0.0293988,0.0214009,0.00007190642,0.904184],"study_design_scores_gemma":[0.0002649477,0.00006141791,0.8206435,0.00002198498,0.0003502493,0.00000491311,0.00004670844,0.1423639,0.01034029,0.02273701,0.002915652,0.0002493628],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4093966,0.00004947662,0.5895562,0.0000973406,0.00005069237,0.00001472327,0.000002696934,0.00001840009,0.0008138424],"genre_scores_gemma":[0.9693776,0.0001450305,0.03027818,0.0001080019,0.000009880167,0.000001932727,0.00002553187,0.000001536349,0.00005231247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9039347,"threshold_uncertainty_score":0.2018425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02195540229017587,"score_gpt":0.2594236243805459,"score_spread":0.2374682220903701,"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."}}