{"id":"W3017632013","doi":"10.1029/2019jd031569","title":"Climatological‐Scale Analysis of Intensive and Semi‐intensive Aerosol Parameters Derived From AERONET Retrievals Over the Arctic","year":2020,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Université de Sherbrooke","funders":"Eurostars; Natural Sciences and Engineering Research Council of Canada; Goddard Space Flight Center; Université de Sherbrooke; National Aeronautics and Space Administration","keywords":"AERONET; Environmental science; Aerosol; Atmospheric sciences; Arctic; Climatology; Effective radius; RADIUS; Amplitude; Oceanography; Meteorology; Geography; Physics; Geology; Astrophysics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004158685,0.0002609807,0.0002380948,0.0006746041,0.0002492297,0.0004882416,0.0001519226,0.0001722275,0.0003111191],"category_scores_gemma":[0.000549452,0.0001440121,0.0002658498,0.0005945395,0.0001379946,0.000234026,0.000232172,0.0001342938,0.0001266725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002852352,"about_ca_system_score_gemma":0.0002150195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03597547,"about_ca_topic_score_gemma":0.04151833,"domain_scores_codex":[0.9998914,0.00001340093,0.000009290455,0.00003895807,0.00002632392,0.00002054252],"domain_scores_gemma":[0.9996656,0.00006546007,0.00006915096,0.0000420475,0.0001203582,0.00003743472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004062191,0.00007863816,0.9323761,0.0000536011,0.0002851174,0.000228573,0.0001673177,0.01639305,0.0351067,0.00007014089,0.0003913842,0.01444322],"study_design_scores_gemma":[0.000005489559,0.00001532469,0.9911349,0.00000324387,0.00002797189,0.00003244836,0.00004659482,0.007294181,0.001185094,0.000009239532,0.0002405286,0.000005046023],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990444,0.00003966683,0.0001851997,0.000005891756,0.000002913998,0.000002115152,0.0004857819,0.00001647226,0.0002176051],"genre_scores_gemma":[0.9985254,0.00003157538,0.0002268758,0.000003047536,0.000004278682,0.00000224447,0.001113662,0.000005235611,0.00008760572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03597547,"threshold_uncertainty_score":0.07153219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04594391980862277,"score_gpt":0.2916421601108853,"score_spread":0.2456982403022626,"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."}}