{"id":"W4415847599","doi":"10.5194/amt-18-6093-2025","title":"Five years of GOSAT-2 retrievals with RemoTeC: XCO <sub>2</sub> and XCH <sub>4</sub> data products with quality filtering by machine learning","year":2025,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Horizon 2020; European Space Agency","keywords":"Flagging; Greenhouse gas; Data set; Filter (signal processing); Data quality; Quality (philosophy); Data processing; Measure (data warehouse)","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"],"consensus_categories":[],"category_scores_codex":[0.001614728,0.0004547725,0.0005520769,0.000005240499,0.0002030743,0.00005381371,0.0006418239,0.0001589591,0.00002404996],"category_scores_gemma":[0.0001855018,0.000400059,0.00004170284,0.0007169282,0.0006080765,0.0004445168,0.0008571431,0.0004301201,0.000005970926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000474086,"about_ca_system_score_gemma":0.00005208175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007886344,"about_ca_topic_score_gemma":0.0001583526,"domain_scores_codex":[0.9963807,0.0002152794,0.0005849869,0.001124043,0.001183004,0.0005120279],"domain_scores_gemma":[0.998179,0.00005856464,0.0004289809,0.001154426,0.00004339421,0.0001356142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003022758,0.0002341705,0.04938136,0.0001592475,0.0001427299,0.0000136674,0.0002054307,0.001146268,0.744265,0.00001266087,0.0009824124,0.2031547],"study_design_scores_gemma":[0.001142429,0.001258739,0.07358778,0.0006349868,0.0002575186,0.0000289899,0.0003435898,0.006471277,0.9077849,0.0001297946,0.007215543,0.001144417],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9266112,0.0005953215,0.07055964,0.0001813606,0.00003317675,0.0009531725,0.00001630131,0.0002850123,0.0007648421],"genre_scores_gemma":[0.9022581,0.001097915,0.09618188,0.000162451,0.00001580223,0.00004224866,0.0000445015,0.00006408933,0.0001330144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2020103,"threshold_uncertainty_score":0.9998451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01554491705195792,"score_gpt":0.223673286748097,"score_spread":0.2081283696961391,"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."}}