{"id":"W4401706432","doi":"10.2139/ssrn.4896293","title":"On Measuring Climate Risks Using Attention Search and Testing the Clean Energy-Climate Hypothesis","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Clean energy; Climate change; Environmental science; Energy (signal processing); Natural resource economics; Environmental protection; Economics; Ecology; Statistics; Mathematics; Biology","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":[0.007426657,0.0009817344,0.001514905,0.002396995,0.000798089,0.002219358,0.001802847,0.002666173,0.004960889],"category_scores_gemma":[0.05279309,0.0003687986,0.0008072793,0.002325599,0.001257365,0.004735994,0.002237355,0.001299847,0.0005039722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008885531,"about_ca_system_score_gemma":0.0009850384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01070744,"about_ca_topic_score_gemma":0.008923095,"domain_scores_codex":[0.9965222,0.002158639,0.0001444653,0.0004922054,0.0004676816,0.0002148257],"domain_scores_gemma":[0.8982205,0.09617122,0.002591379,0.001635327,0.0008206481,0.0005609834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003092516,0.002286282,0.3234154,0.0006281773,0.001617368,0.0005086361,0.0008084948,0.1828752,0.00874659,0.07966823,0.00624468,0.3901084],"study_design_scores_gemma":[0.0002872641,0.001636109,0.1492901,0.0001392252,0.0004393793,0.0003035806,0.0009197264,0.6640333,0.005042413,0.1762862,0.001468994,0.0001536479],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8868711,0.001367286,0.08772231,0.003682589,0.0001829716,0.0001613873,0.0003214392,0.0001954227,0.01949545],"genre_scores_gemma":[0.9831969,0.0004734003,0.01475475,0.0004205196,0.0001523749,0.00008376749,0.0001838483,0.00001779999,0.0007167644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01070744,"threshold_uncertainty_score":0.03927642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1030387667125404,"score_gpt":0.2912771133476055,"score_spread":0.1882383466350652,"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."}}