{"id":"W3163323583","doi":"10.33002/jelp001.04","title":"ASSESSMENT OF ENVIRONMENTAL DAMAGE AND POLICY ACTIONS BY USING CONTINGENT VALUATION METHOD: AN EMPIRICAL ANALYSIS OF SAGO INDUSTRIAL POLLUTION IN TAMIL NADU, INDIA","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mitacs; Université de Montréal; Indian Council of Social Science Research","keywords":"Livestock; Agriculture; Stratified sampling; Tamil; Contingent valuation; Pollution; Production (economics); Business; Willingness to pay; Water quality; Natural resource economics; Environmental planning; Agricultural economics; Environmental science; Geography; Economics; Forestry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001217465,0.0003168366,0.000323641,0.00174376,0.0008459445,0.001207869,0.0009507448,0.00057424,0.001153896],"category_scores_gemma":[0.003663263,0.0002705431,0.0006850316,0.002340698,0.001228698,0.0007447074,0.0008543419,0.0009649685,0.000105729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00270028,"about_ca_system_score_gemma":0.001126017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05720916,"about_ca_topic_score_gemma":0.05597588,"domain_scores_codex":[0.9992226,0.000420958,0.00003642961,0.0000512366,0.00008274339,0.0001859407],"domain_scores_gemma":[0.993277,0.004778517,0.001059552,0.0002485413,0.0003537297,0.000282654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004407945,0.0009968419,0.915557,0.0001528393,0.0003763953,0.003565804,0.004293124,0.05544031,0.0009567646,0.005486799,0.0009270788,0.01180621],"study_design_scores_gemma":[0.00002412028,0.0004207913,0.8682569,0.00004387569,0.0001615041,0.000477102,0.01209149,0.1158824,0.0005366821,0.001412508,0.0006402142,0.00005242401],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992372,0.00002375448,0.0001732254,0.00005470487,8.934775e-7,0.00001247267,0.00005222758,0.000002431736,0.0004429417],"genre_scores_gemma":[0.9996669,0.00003672543,0.0001228652,0.000006240491,0.000001384496,0.000009404214,0.00005241784,7.32469e-7,0.0001031691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05720916,"threshold_uncertainty_score":0.1137524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1022695396990083,"score_gpt":0.3420830882467504,"score_spread":0.2398135485477421,"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."}}