{"id":"W2126050522","doi":"10.1002/jgrd.50826","title":"Application of OMI, SCIAMACHY, and GOME‐2 satellite SO<sub>2</sub> retrievals for detection of large emission sources","year":2013,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Environment and Climate Change Canada","funders":"Earth Sciences Division; European Organization for the Exploitation of Meteorological Satellites; Korea Meteorological Administration","keywords":"SCIAMACHY; Ozone Monitoring Instrument; Environmental science; Satellite; Remote sensing; Spectrometer; Ozone; Atmospheric sciences; Meteorology; Physics; Geography; 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":[0.0007068837,0.0005308664,0.0003821458,0.001405296,0.0003830392,0.000473638,0.0003999715,0.0006564817,0.0004224811],"category_scores_gemma":[0.001152366,0.0002966419,0.000438687,0.0009585127,0.0002336777,0.0005005813,0.0004432871,0.0002311558,0.0001101792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004773419,"about_ca_system_score_gemma":0.0006695425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01332629,"about_ca_topic_score_gemma":0.02757088,"domain_scores_codex":[0.9997516,0.00004623864,0.00001388424,0.00005674648,0.00007651746,0.0000550246],"domain_scores_gemma":[0.9997171,0.00008983181,0.00004598121,0.00002929002,0.00008116378,0.00003665361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001709926,0.0003171302,0.3227476,0.0003137029,0.0005592292,0.0003572634,0.0005303887,0.04294109,0.3735296,0.0007817494,0.002007433,0.254205],"study_design_scores_gemma":[0.0002308292,0.0002644024,0.6274042,0.00002074825,0.0002755504,0.000104141,0.0004535829,0.3003846,0.06811421,0.0005182278,0.002154798,0.0000747147],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902691,0.0002822962,0.007128022,0.0001204669,0.0000280509,0.0000539223,0.0004827808,0.0002174603,0.001417934],"genre_scores_gemma":[0.9759517,0.0001162152,0.02290216,0.00004242926,0.00002204568,0.00003002502,0.0006106896,0.00002087458,0.0003040159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01332629,"threshold_uncertainty_score":0.02649742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01607149427401802,"score_gpt":0.2783727183890208,"score_spread":0.2623012241150028,"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."}}