{"id":"W4249340297","doi":"10.31223/x5rc82","title":"Saving the world from your couch: The heterogeneous medium-run benefits of COVID-19 lockdowns on air pollution","year":2020,"lang":"en","type":"preprint","venue":"","topic":"COVID-19 impact on air quality","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Leverage (statistics); Coronavirus disease 2019 (COVID-19); Air quality index; Globe; Pandemic; Pollution; Recession; Air pollution; Natural resource economics; Economic impact analysis; Short run; Economic recovery; Business; Economics; Development economics; Geography; Macroeconomics; Meteorology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001204008,0.0005684113,0.0005629668,0.00007191941,0.0003869676,0.00008737123,0.002112701,0.0002714483,0.007064272],"category_scores_gemma":[0.001145448,0.0003381007,0.0004019858,0.0004762652,0.0006253984,0.00008306752,0.003201692,0.001072208,0.0005772066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001627844,"about_ca_system_score_gemma":0.000313422,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03081593,"about_ca_topic_score_gemma":0.02299528,"domain_scores_codex":[0.9954144,0.0006502531,0.0007954905,0.001073428,0.001539576,0.0005268099],"domain_scores_gemma":[0.9956447,0.001223003,0.0006651375,0.00196576,0.00001745853,0.0004839553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003127473,0.0002980885,0.01365043,0.0001344465,0.0002439797,0.00001879275,0.007193685,0.9317099,0.002969997,0.0006734671,0.03635231,0.006442168],"study_design_scores_gemma":[0.001799716,0.0003883679,0.7414169,0.0003264877,0.0005529613,0.00001987352,0.001710372,0.01381602,0.02902131,0.01925432,0.18938,0.002313659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6327712,0.0003739024,0.004943719,0.3473149,0.001362018,0.002736304,0.001949245,0.0004854728,0.00806326],"genre_scores_gemma":[0.9352413,0.00006632776,0.0002238849,0.06350745,0.0002484742,0.00004052147,0.00006976134,0.00004533544,0.0005569283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9178939,"threshold_uncertainty_score":0.9999071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0882315252299581,"score_gpt":0.3329863300334523,"score_spread":0.2447548048034942,"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."}}