{"id":"W2949347793","doi":"10.5194/amt-12-5443-2019","title":"Improving the TROPOMI CO data product: update of the spectroscopic database and destriping of single orbits","year":2019,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; Bundesministerium für Wirtschaft und Energie; European Commission","keywords":"HITRAN; SCIAMACHY; Environmental science; Troposphere; Satellite; Remote sensing; Database; Meteorology; Spectroscopy; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001198761,0.0001742393,0.0002654635,0.000001955986,0.0001413138,0.00003628699,0.000831746,0.00003835034,0.0003841888],"category_scores_gemma":[0.000130753,0.00009734741,0.00003980027,0.0002939031,0.0002177133,0.0003059623,0.0001480438,0.0001469556,0.000009593952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001263848,"about_ca_system_score_gemma":0.0001146425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001729469,"about_ca_topic_score_gemma":0.0002722923,"domain_scores_codex":[0.9982327,0.0001265864,0.0003897925,0.0003860383,0.0005981152,0.0002668008],"domain_scores_gemma":[0.9981578,0.00005110317,0.0003755455,0.001279388,0.00009438579,0.00004174953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008105573,0.0001068114,0.6699298,0.0003968192,0.00009628333,0.00000202444,0.0001815933,0.0000527688,0.1934445,0.0001086517,0.001763666,0.133836],"study_design_scores_gemma":[0.0009399584,0.001030436,0.2231725,0.0007835254,0.0003467194,0.00003481758,0.0008893846,0.02169974,0.733588,0.0005014215,0.01617657,0.0008369055],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869318,0.003770372,0.003269605,0.0003723173,0.0002476748,0.001359138,0.00008683342,0.0001058947,0.003856384],"genre_scores_gemma":[0.9661102,0.0002694725,0.03335619,0.0001313627,0.00004550768,0.000002843148,0.00003166176,0.000008215745,0.00004457936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5401436,"threshold_uncertainty_score":0.4206598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03368391287215884,"score_gpt":0.2299306734642416,"score_spread":0.1962467605920827,"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."}}