{"id":"W3187204636","doi":"10.2139/ssrn.3874366","title":"Impacts of the Clean Air Act on the Power Sector from 1938-1994: Anticipation and Adaptation","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Anticipation (artificial intelligence); Clean Air Act; Adaptation (eye); Power (physics); Environmental science; Business; Air pollution; Computer science; Psychology; Chemistry; Artificial intelligence","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":[0.00071316,0.0001940606,0.0002057289,0.0006190425,0.0006254448,0.002154838,0.0003797763,0.001047606,0.00453481],"category_scores_gemma":[0.004205919,0.0001264179,0.0003062419,0.002304571,0.0009764517,0.001226058,0.001425767,0.001516367,0.0003113707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00378071,"about_ca_system_score_gemma":0.002527015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1554497,"about_ca_topic_score_gemma":0.2125772,"domain_scores_codex":[0.9994081,0.00008882156,0.00002358399,0.00005430671,0.0000841333,0.0003411008],"domain_scores_gemma":[0.9981776,0.0004142775,0.000885671,0.00004933939,0.0003002509,0.0001728628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003036871,0.0006928585,0.7813554,0.0003001236,0.0003384882,0.002032025,0.005187465,0.02562571,0.001958353,0.0727208,0.03155124,0.07520071],"study_design_scores_gemma":[0.00002200074,0.0001968581,0.956163,0.00008368617,0.00008737951,0.00009568204,0.007117936,0.002773513,0.0009825089,0.006923706,0.02551509,0.00003867626],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9547321,0.001549799,0.0003487901,0.0160976,0.0002077145,0.00002072545,0.002293495,0.00001936203,0.02473047],"genre_scores_gemma":[0.9972052,0.0006843289,0.00002954335,0.000343644,0.00006213092,0.000004732678,0.0003128983,0.000003240445,0.001354215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1554497,"threshold_uncertainty_score":0.3090897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0178947716928419,"score_gpt":0.1986815520612411,"score_spread":0.1807867803683992,"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."}}