{"id":"W3216654809","doi":"10.2139/ssrn.3934621","title":"State Attorneys General: Empowering the Clean Energy Future","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Environmental law and policy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Impact","funders":"","keywords":"State (computer science); Clean energy; Clean Water Act; Political science; Energy (signal processing); Law and economics; Law; Business; Environmental economics; Economics; Computer science; Physics","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":[0.005889212,0.000401424,0.0004993772,0.0006921386,0.008434996,0.008638578,0.00128933,0.03455122,0.01551347],"category_scores_gemma":[0.0126669,0.0006902392,0.0006254266,0.0009769055,0.003003873,0.006235725,0.005091452,0.01639017,0.002425868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004037425,"about_ca_system_score_gemma":0.01928288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05145707,"about_ca_topic_score_gemma":0.1078057,"domain_scores_codex":[0.9956163,0.000774526,0.0002078597,0.0005862492,0.001281519,0.001533644],"domain_scores_gemma":[0.9949818,0.002841903,0.0003569676,0.0002580909,0.0007149569,0.0008461733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001488967,0.0002358503,0.00452418,0.0001692527,0.00003327873,0.0008369773,0.003273853,0.0006424366,0.001840377,0.4496723,0.5104709,0.02815167],"study_design_scores_gemma":[0.00008021419,0.00007972305,0.008356151,0.0003292779,0.00006567959,0.0001756373,0.002845398,0.0009950353,0.001490164,0.04741723,0.9380683,0.00009723566],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.04292436,0.01019972,0.001273673,0.7032241,0.008097039,0.000108713,0.0003946189,0.0001974359,0.2335804],"genre_scores_gemma":[0.3213222,0.006340267,0.001160966,0.2967129,0.004035847,0.0001794813,0.0001984458,0.0001369643,0.3699129],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.05145707,"threshold_uncertainty_score":0.1023151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004499488727067207,"score_gpt":0.2542409268214106,"score_spread":0.2497414380943434,"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."}}