{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002586868,0.0005236077,0.0004936452,0.002090914,0.0008585275,0.00239384,0.0007259214,0.0008153851,0.006062051],"category_scores_gemma":[0.007109046,0.0003172041,0.0005913461,0.002258883,0.002645841,0.006225846,0.001663303,0.001338023,0.0009083594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008612604,"about_ca_system_score_gemma":0.0004793292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003131235,"about_ca_topic_score_gemma":0.0003438257,"domain_scores_codex":[0.9965833,0.001597857,0.0002753749,0.0004697872,0.0008556284,0.0002180935],"domain_scores_gemma":[0.9946588,0.003319878,0.0006882716,0.000462735,0.0005566436,0.0003135971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008891247,0.00003779501,0.001496669,0.0001353418,0.00003245279,0.0001989708,0.0008152701,0.004555389,0.00290352,0.9578627,0.001166745,0.03070614],"study_design_scores_gemma":[0.00003484954,0.0001428632,0.001155716,0.00008953128,0.00002424993,0.0006296019,0.0009256026,0.02497164,0.002470789,0.9496806,0.01983345,0.00004102345],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.142954,0.001070561,0.7892829,0.001998563,0.00009875136,0.0001865658,0.0005948814,0.0001542628,0.06365961],"genre_scores_gemma":[0.7741467,0.000724745,0.2175656,0.0003673004,0.000247898,0.0002531113,0.0006782627,0.00005598166,0.005960368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006062051,"threshold_uncertainty_score":0.02027959,"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."}}