{"id":"W4408426679","doi":"10.5194/egusphere-egu25-10762","title":"Using Arctic field data and remote sensing BrO data to constrain blowing snow sea salt aerosol production parameterizations","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Methane Hydrates and Related Phenomena","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"3v Geomatics (Canada); University of Alberta; Environment and Climate Change Canada; University of Toronto","funders":"","keywords":"Snow; Aerosol; Arctic; Environmental science; The arctic; Meteorology; Remote sensing; Climatology; Field (mathematics); Atmospheric sciences; Oceanography; Geography; Geology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001182551,0.0006121825,0.0003031369,0.001304394,0.0003565873,0.0007680521,0.0003995053,0.0004123002,0.0005863033],"category_scores_gemma":[0.00214654,0.0002385196,0.000653971,0.0009417093,0.0001766858,0.0006427109,0.000366618,0.0002888514,0.0002266947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005775677,"about_ca_system_score_gemma":0.000636056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.085261,"about_ca_topic_score_gemma":0.09808863,"domain_scores_codex":[0.9996676,0.00006718288,0.00003175705,0.0001205692,0.0000687471,0.00004417818],"domain_scores_gemma":[0.9990472,0.0003665087,0.0001560873,0.0001371243,0.0002340988,0.00005888012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002582092,0.0002111897,0.8048467,0.0002541734,0.0003451734,0.0003321091,0.0003562861,0.1402918,0.01335879,0.0007579717,0.001398448,0.03758937],"study_design_scores_gemma":[0.00004954509,0.00009648626,0.6790859,0.00009377865,0.0001428448,0.00009722391,0.0003442281,0.3095956,0.004796666,0.0004967236,0.005138046,0.00006296517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898019,0.0001958318,0.004046394,0.00003172673,0.00001446318,0.00002740691,0.004270943,0.0001444749,0.00146682],"genre_scores_gemma":[0.9773043,0.0001687748,0.01145015,0.00003069958,0.00001538433,0.00004194875,0.01075061,0.00003447308,0.0002037079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.085261,"threshold_uncertainty_score":0.1695294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09625933332049498,"score_gpt":0.3214921231626917,"score_spread":0.2252327898421967,"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."}}