{"id":"W4220724133","doi":"10.1787/8f030bcc-en","title":"Carbon pricing and COVID-19","year":2022,"lang":"en","type":"report","venue":"OECD environment working papers","topic":"Energy, Environment, and Transportation Policies","field":"Energy","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Treasury; Environment and Climate Change Canada; Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz; Australian Government","keywords":"Scope (computer science); Greenhouse gas; Carbon price; Climate change; Coronavirus disease 2019 (COVID-19); Natural resource economics; Aviation; Business; Environmental economics; Economics; Computer science; Engineering; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000577914,0.0006613825,0.0006299753,0.0002413485,0.0005031865,0.0000516381,0.0002933737,0.0003695757,0.007155344],"category_scores_gemma":[0.00003862031,0.000715226,0.0002193973,0.0000878685,0.0002983611,0.00003374245,0.0001781176,0.0006916088,0.00003441496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001774914,"about_ca_system_score_gemma":0.0001980593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005819656,"about_ca_topic_score_gemma":0.0005890784,"domain_scores_codex":[0.9960586,0.000143668,0.0006585379,0.001028787,0.001458631,0.0006517647],"domain_scores_gemma":[0.9981552,0.0001980216,0.0004988164,0.0007243485,0.000002350207,0.0004212848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001443761,0.0004758577,0.1012175,0.001031779,0.001890494,0.001002347,0.005632895,0.7896955,0.003067172,0.007336925,0.004423264,0.0840819],"study_design_scores_gemma":[0.0004927037,0.00005164536,0.009794912,0.00004115348,0.0002916305,0.00003196426,0.0003944824,0.0000348785,0.00005425567,0.0001085939,0.9879667,0.0007370511],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0207638,0.005703492,0.00002678846,0.0006154881,0.00098728,0.0004424972,0.00004458975,0.0002802702,0.9711358],"genre_scores_gemma":[0.8508297,0.07099039,0.0003247794,0.002031332,0.0007063173,0.0004052485,0.001108764,0.0003697185,0.07323382],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9835435,"threshold_uncertainty_score":0.9995299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0281228010670733,"score_gpt":0.2468003510195627,"score_spread":0.2186775499524894,"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."}}