{"id":"W4407797604","doi":"10.5194/tc-19-769-2025","title":"Improving large-scale snow albedo modeling using a climatology of light-absorbing particle deposition","year":2025,"lang":"en","type":"article","venue":"˜The œcryosphere","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Horizon 2020; European Commission","keywords":"Snow; Albedo (alchemy); Particle deposition; Environmental science; Deposition (geology); Atmospheric sciences; Scale (ratio); Climatology; Particle (ecology); Meteorology; Aerosol; Geography; Geology; Oceanography; Geomorphology; Cartography","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":[],"consensus_categories":[],"category_scores_codex":[0.0002356861,0.0001558212,0.0002155078,0.000001037338,0.00034214,0.00003180585,0.0002705028,0.0001087407,0.0005052086],"category_scores_gemma":[0.00001378408,0.0001237491,0.00009784934,0.0003167379,0.00009641531,0.0001534295,0.0003160771,0.0001564967,0.00004626665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001073591,"about_ca_system_score_gemma":0.00002375347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002057801,"about_ca_topic_score_gemma":0.002074767,"domain_scores_codex":[0.99864,0.00006823933,0.0003546294,0.0003160573,0.0001902613,0.0004308341],"domain_scores_gemma":[0.9993966,0.00004551186,0.0001308261,0.0003542405,0.00001520956,0.00005766302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001608057,0.000497187,0.3123487,0.0001442369,0.0001022116,0.00002066585,0.004309,0.2864231,0.3610383,0.001259253,0.0005922997,0.03310424],"study_design_scores_gemma":[0.0003577947,0.00003216055,0.001438334,0.00005892931,0.00006302605,0.000009513534,0.001368318,0.9871462,0.008584512,0.0006043441,0.0001805151,0.0001563537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8032414,0.0002447737,0.1938327,0.0001615924,0.0001239519,0.000157948,0.000001452804,0.00003704653,0.002199158],"genre_scores_gemma":[0.9914188,0.00001015463,0.007767011,0.0002807746,0.00002672054,0.00001437147,0.000001271979,0.00001794043,0.0004629416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7007231,"threshold_uncertainty_score":0.553168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008070543930799543,"score_gpt":0.2274374189870552,"score_spread":0.2193668750562557,"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."}}