{"id":"W2050141487","doi":"10.1080/07038992.2000.10874778","title":"Aerosol Optical Depth for Atmospheric Correction of AVHRR Composite Data","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Goddard Space Flight Center; York University","keywords":"Forestry; Humanities; Geography; Physics; Cartography; Art","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002016554,0.0001170326,0.0002069741,0.000004248066,0.0001286502,0.00002151557,0.0002570696,0.00007042916,0.0002992045],"category_scores_gemma":[0.00003271799,0.0001143978,0.00007259311,0.0001310966,0.0002473442,0.0002030417,0.00002789785,0.000150225,0.00001840384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003221359,"about_ca_system_score_gemma":0.00007377063,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007228997,"about_ca_topic_score_gemma":0.0136327,"domain_scores_codex":[0.999023,0.00002526405,0.0003328167,0.0001685264,0.0001701069,0.0002802453],"domain_scores_gemma":[0.9991352,0.00004012468,0.000162356,0.0002704949,0.00001039159,0.0003814373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003551547,0.000006250657,0.001489032,0.000003692065,0.00001726053,0.00003566491,0.0001248817,0.05895416,0.001170258,4.520153e-7,0.000808219,0.9373546],"study_design_scores_gemma":[0.0005386816,0.0002078429,0.01119053,0.000088642,0.00009061732,0.0007481549,0.0002155527,0.9522388,0.00078206,0.00006397314,0.03360203,0.0002331417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7961127,0.00007342873,0.1981385,0.0001289204,0.000410449,0.0001099709,0.00000395369,0.000004375493,0.005017739],"genre_scores_gemma":[0.5564721,0.0000296529,0.4427316,0.000133464,0.00006285616,8.561833e-9,0.000005270579,0.00001749789,0.0005475477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9371215,"threshold_uncertainty_score":0.999382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01310389634367046,"score_gpt":0.2150398832970496,"score_spread":0.2019359869533792,"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."}}