{"id":"W4221010818","doi":"10.1029/2021jd035597","title":"Deep Learning to Evaluate US NO <sub>x</sub> Emissions Using Surface Ozone Predictions","year":2022,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; National Aeronautics and Space Administration","keywords":"Ozone; Satellite; Environmental science; Atmospheric sciences; Ozone Monitoring Instrument; Emission inventory; Climatology; Air quality index; Geography; Meteorology; Physics; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001077522,0.00115367,0.0004365083,0.0005072194,0.0002361115,0.0005760242,0.0006796009,0.0009782867,0.00159656],"category_scores_gemma":[0.001954986,0.0003015453,0.0005768242,0.0003392998,0.0003209353,0.0006610592,0.0006883856,0.0009874137,0.0002945923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009708456,"about_ca_system_score_gemma":0.001151503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03082931,"about_ca_topic_score_gemma":0.02044001,"domain_scores_codex":[0.9998154,0.00004545887,0.00001171406,0.0000532104,0.00002654188,0.00004765271],"domain_scores_gemma":[0.9993852,0.0003227162,0.00006154203,0.000036207,0.0001241584,0.00007011196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003668552,0.0007109785,0.06249561,0.0000761403,0.0002501783,0.00008676144,0.00002296725,0.8801168,0.001981504,0.0004280517,0.003505105,0.04995913],"study_design_scores_gemma":[0.000008176891,0.00003505671,0.001632793,0.000003548847,0.000007504803,0.000001898543,0.000006471409,0.9976715,0.0004362096,0.0001354225,0.00005896622,0.000002348259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9809187,0.0004973583,0.01423011,0.0006223869,0.00008564279,0.00004277145,0.0009462126,0.0007514503,0.001905332],"genre_scores_gemma":[0.9945717,0.00006847784,0.003262498,0.000109946,0.000016111,0.00001865696,0.001221342,0.00001409027,0.0007172035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03082931,"threshold_uncertainty_score":0.06129974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07924959017847694,"score_gpt":0.3911493700390782,"score_spread":0.3118997798606012,"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."}}