{"id":"W1797561859","doi":"10.1002/2013jd021279","title":"Remote sensing of aerosols in the Arctic for an evaluation of global climate model simulations","year":2014,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Vetenskapsrådet; National Aeronautics and Space Administration; Norges Forskningsråd; Stockholms Universitet; National Science Council; Svenska Forskningsrådet Formas","keywords":"Environmental science; Aerosol; Moderate-resolution imaging spectroradiometer; Climatology; Subarctic climate; Arctic; AERONET; Atmospheric sciences; Climate model; Satellite; Latitude; Sun photometer; Remote sensing; Meteorology; Climate change; Geography; Geology; Oceanography","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":[0.004226796,0.00009294212,0.0002518854,0.000001084332,0.0001156866,0.00002554168,0.00036871,0.00005487055,0.00005062283],"category_scores_gemma":[0.001194708,0.00006220945,0.0001246326,0.0004313717,0.0003147032,0.0002240822,0.00009055473,0.0002341144,0.000003608288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001991639,"about_ca_system_score_gemma":0.00009631193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001139248,"about_ca_topic_score_gemma":0.0008816834,"domain_scores_codex":[0.9966583,0.0006190613,0.0004936602,0.0001512095,0.001743574,0.0003341572],"domain_scores_gemma":[0.998284,0.0006827556,0.0002962155,0.0002712576,0.0003723519,0.00009345118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004320443,0.0004249001,0.01847216,0.00004916695,0.00002512584,0.00000111829,0.000627444,0.8701019,0.01337545,0.002674472,0.0001667247,0.09364948],"study_design_scores_gemma":[0.0005658779,0.0006828204,0.0715913,0.00005230692,0.00002828896,0.000002358993,0.0002642341,0.846824,0.0002204197,0.07970341,0.0000128107,0.00005217882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9736415,0.00002235302,0.02505941,0.0001925769,0.00002330211,0.0003084178,0.000005460653,0.000001702276,0.0007452568],"genre_scores_gemma":[0.9777055,0.000007471471,0.02216172,0.00002884392,0.00007933874,8.565733e-7,8.153106e-7,0.000008226074,0.000007206477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09359729,"threshold_uncertainty_score":0.2536828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07626031536549431,"score_gpt":0.3872356606675146,"score_spread":0.3109753453020203,"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."}}