{"id":"W2581428632","doi":"10.1109/jstars.2017.2650144","title":"Long Temporal Analysis of 3-km MODIS Aerosol Product Over East China","year":2017,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"AERONET; Aerosol; Environmental science; Moderate-resolution imaging spectroradiometer; Climatology; Correlation coefficient; Remote sensing; Deep blue; Atmospheric sciences; Meteorology; Satellite; Geography; Geology; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005460097,0.0003090691,0.000217824,0.0009223555,0.0003088668,0.0003451583,0.0002368682,0.0001741112,0.0002463801],"category_scores_gemma":[0.0003943272,0.0001096009,0.0002777003,0.001230495,0.0001171887,0.0002706902,0.0002193799,0.0001176811,0.00009009102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005823264,"about_ca_system_score_gemma":0.0007906718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06190298,"about_ca_topic_score_gemma":0.0640947,"domain_scores_codex":[0.9998155,0.00001372241,0.00001933069,0.00005250028,0.00006716854,0.00003179393],"domain_scores_gemma":[0.9996787,0.00002571594,0.00005597408,0.00002893381,0.0001761268,0.00003447135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002895363,0.0002056629,0.8654694,0.0002330184,0.0003051498,0.001657059,0.0005779154,0.02097778,0.03347341,0.0005943667,0.00299636,0.07322042],"study_design_scores_gemma":[0.00000792224,0.00002835249,0.9612989,0.000008431637,0.00005596212,0.00007362342,0.0001255713,0.03484134,0.0023124,0.00006462206,0.001167237,0.00001557023],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99799,0.0001037625,0.0005638321,0.00002439753,0.00000590961,0.000007713776,0.0008843327,0.00003371948,0.0003861544],"genre_scores_gemma":[0.9964558,0.00007412986,0.001039481,0.00001201381,0.000006312537,0.0000101274,0.002093578,0.000008593307,0.0002999732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06190298,"threshold_uncertainty_score":0.1230853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890050862286069,"score_gpt":0.237889924978611,"score_spread":0.2189894163557503,"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."}}