{"id":"W2025363231","doi":"10.1007/s10640-008-9219-7","title":"Comparing Fuzzy and Probabilistic Approaches to Preference Uncertainty in Non-Market Valuation","year":2008,"lang":"en","type":"article","venue":"Environmental and Resource Economics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Forest Service","funders":"BIOCAP Canada","keywords":"Amenity; Fuzzy logic; Valuation (finance); Willingness to pay; Econometrics; Contingent valuation; Economics; Preference; Actuarial science; Computer science; Mathematics; Microeconomics; Artificial intelligence","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":[0.01989948,0.0008011639,0.001147527,0.003040442,0.0008189119,0.005048307,0.00210559,0.001833244,0.00521941],"category_scores_gemma":[0.09817951,0.0006280225,0.001631777,0.003538841,0.003013767,0.00965281,0.002062141,0.002312751,0.0001852459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003178518,"about_ca_system_score_gemma":0.001413724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003399081,"about_ca_topic_score_gemma":0.004740437,"domain_scores_codex":[0.9898722,0.007314,0.0003613099,0.0003277644,0.001834291,0.0002904516],"domain_scores_gemma":[0.8420151,0.1496982,0.002296393,0.002388826,0.002883008,0.0007184263],"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":[0.0009104897,0.000316695,0.004596745,0.0005195442,0.0004957045,0.0001414282,0.001106269,0.224369,0.0003369131,0.6821964,0.001426632,0.0835842],"study_design_scores_gemma":[0.00008790135,0.0001482171,0.003263535,0.00008360962,0.0001051412,0.00007462082,0.000579206,0.3680217,0.000163268,0.6263912,0.001016859,0.00006474704],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2976159,0.006007872,0.6736228,0.003578489,0.0002264501,0.0001860551,0.0002315853,0.00007323341,0.01845751],"genre_scores_gemma":[0.9438837,0.001293233,0.05326694,0.0001365841,0.0001889929,0.0001251731,0.0001046879,0.00002525952,0.0009754406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01989948,"threshold_uncertainty_score":0.1052398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.203194047608541,"score_gpt":0.1819566879136579,"score_spread":0.02123735969488313,"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."}}