{"id":"W4399943367","doi":"10.1073/pnas.2317077121","title":"Quantifying NO <sub>x</sub> point sources with Landsat and Sentinel-2 satellite observations of NO <sub>2</sub> plumes","year":2024,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"GHGSat (Canada)","funders":"Samsung Advanced Institute of Technology","keywords":"Plume; Environmental science; Remote sensing; Satellite; Point source; Ozone Monitoring Instrument; Satellite imagery; Ozone; Atmospheric sciences; Meteorology; Geology; Geography; Physics; Optics","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":[0.0001643606,0.0003831038,0.0001386741,0.0004394327,0.0001509348,0.0003384871,0.000243014,0.0002755979,0.0003248915],"category_scores_gemma":[0.0002437361,0.0001675255,0.0002476193,0.0003427915,0.0001053076,0.0003295715,0.0003817485,0.0001813275,0.0001149553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003265556,"about_ca_system_score_gemma":0.0002370532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0157972,"about_ca_topic_score_gemma":0.0336656,"domain_scores_codex":[0.9999117,0.000007724399,0.000004120065,0.00002386336,0.00003447084,0.00001812376],"domain_scores_gemma":[0.9998721,0.00001622908,0.00004607346,0.00001144322,0.00004248698,0.00001166371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003592061,0.00021422,0.6101261,0.0001234241,0.0002321211,0.0002856492,0.0003755282,0.0363653,0.3188096,0.0004650751,0.0009881506,0.03165558],"study_design_scores_gemma":[0.00004187793,0.0001311825,0.8112238,0.00002018154,0.0001261483,0.0001623929,0.0003158334,0.1207309,0.06401032,0.0004044322,0.002785417,0.0000475427],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956328,0.00008759362,0.002782719,0.00003546498,0.000004078766,0.00001302993,0.0005244992,0.00006820464,0.0008515707],"genre_scores_gemma":[0.9889587,0.0001175372,0.009176952,0.00003055382,0.000008070577,0.00001848893,0.001183601,0.00001818615,0.00048795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0157972,"threshold_uncertainty_score":0.03141052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.025202050608292,"score_gpt":0.238346425548983,"score_spread":0.213144374940691,"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."}}