{"id":"W2065848464","doi":"10.1016/j.reseneeco.2014.12.001","title":"Modeling non-compensatory preferences in environmental valuation","year":2014,"lang":"en","type":"article","venue":"Resource and Energy Economics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Cutoff; Econometrics; Valuation (finance); Function (biology); Contingent valuation; Preference; Statistics; Willingness to pay; Mathematics; Economics; Computer science; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003274949,0.0006105871,0.0007980958,0.0005664109,0.0004413209,0.002404391,0.001487975,0.001930989,0.005138656],"category_scores_gemma":[0.01265251,0.0006197699,0.0008198141,0.0009154057,0.001348529,0.003814418,0.001290355,0.001837682,0.0003027551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001488087,"about_ca_system_score_gemma":0.0008288895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004830858,"about_ca_topic_score_gemma":0.005445503,"domain_scores_codex":[0.99903,0.0006398154,0.00003260786,0.00008919056,0.00007707221,0.0001313569],"domain_scores_gemma":[0.9934036,0.005336615,0.000384402,0.0002545347,0.0002357474,0.0003850617],"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":[0.0001043123,0.0001547731,0.001901135,0.00005741078,0.00004816999,0.0002765232,0.000140102,0.6602689,0.0005640702,0.3272576,0.0007355557,0.008491504],"study_design_scores_gemma":[0.00001356506,0.00001594341,0.0002657499,0.000005862039,0.000009181947,0.00002622972,0.00004257237,0.90052,0.00005947732,0.09878108,0.0002526317,0.000007777977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.505367,0.0008140728,0.4647548,0.002482582,0.0001705351,0.00006612205,0.0001505352,0.0001091033,0.02608523],"genre_scores_gemma":[0.9768955,0.0001960427,0.01315785,0.00008581164,0.00004490751,0.00003427682,0.00002904727,0.00002087549,0.009535736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005138656,"threshold_uncertainty_score":0.0173198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04841259576562225,"score_gpt":0.178110273264307,"score_spread":0.1296976774986847,"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."}}