{"id":"W2908039513","doi":"10.1007/s11356-018-3947-1","title":"Revisiting the social cost of carbon after INDC implementation in Malaysia: 2050","year":2019,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Universiti Tenaga Nasional","keywords":"Per capita; Climate change; Greenhouse gas; Natural resource economics; Scenario analysis; Environmental science; Environmental resource management; Business; Economics; Ecology; Population","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":[0.00265545,0.00004974024,0.0001057857,0.0002177644,0.0001628229,0.00004313515,0.0001415316,0.00003308139,0.0005650281],"category_scores_gemma":[0.00001832818,0.00004732232,0.00001872544,0.0002993846,0.0004307621,0.0001971494,0.00015995,0.0001300981,0.0001044551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002931014,"about_ca_system_score_gemma":0.00001254661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009307531,"about_ca_topic_score_gemma":0.00004238898,"domain_scores_codex":[0.9991145,0.00002500954,0.0002452445,0.0002170977,0.00009477327,0.0003033679],"domain_scores_gemma":[0.9997492,0.00002394555,0.00008239123,0.0001060963,0.000004525889,0.00003377736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003072121,0.00002632417,0.965588,0.00002293048,0.000004231967,6.729583e-7,0.004756805,0.0000250479,0.00476972,0.01623505,0.00002902715,0.008511412],"study_design_scores_gemma":[0.0002910421,0.0000281773,0.9893198,0.000007062612,5.940867e-7,0.000001117748,0.004580475,0.00120566,0.0005071159,0.001273411,0.002711982,0.00007353445],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992691,0.0002671545,6.836188e-7,0.002081314,0.00004450342,0.0002609748,0.00008477495,0.000001434363,0.004568117],"genre_scores_gemma":[0.9993902,0.0003079622,0.000006516476,0.0001099115,0.00006876259,0.00002794503,0.000005385443,0.000004011333,0.00007925143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02373177,"threshold_uncertainty_score":0.6186662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08391879421034364,"score_gpt":0.3468264377201106,"score_spread":0.2629076435097669,"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."}}