{"id":"W4285222887","doi":"10.1109/jstars.2022.3177081","title":"A Bayesian Deep Image Prior Downscaling Approach for High-Resolution Soil Moisture Estimation","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Downscaling; Remote sensing; Moderate-resolution imaging spectroradiometer; Computer science; Image resolution; Convolutional neural network; Environmental science; Artificial intelligence; Satellite; Meteorology; Precipitation; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005540529,0.0001558869,0.0002545249,0.0001423443,0.0005764396,0.00006258718,0.00009544195,0.00009579349,0.000004805547],"category_scores_gemma":[0.00008026828,0.0001461844,0.00005560971,0.000661273,0.00007509448,0.0001252355,0.0000491649,0.0004896899,3.119502e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002156161,"about_ca_system_score_gemma":0.00005524238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003646235,"about_ca_topic_score_gemma":0.0006838529,"domain_scores_codex":[0.998522,0.00007413856,0.0005062062,0.0002351542,0.0003983738,0.0002640959],"domain_scores_gemma":[0.9992924,0.00007867061,0.0003438659,0.0001310364,0.00008354827,0.0000704401],"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":[0.0001143885,0.00006952618,0.0002878374,0.00004105715,0.0000337389,0.00001403441,0.001216818,0.4484886,0.07066063,0.00009153768,0.0001638704,0.4788179],"study_design_scores_gemma":[0.00100614,0.0000847093,0.04014968,0.00002892397,0.00005543104,0.0001928445,0.000354963,0.9520226,0.002495722,0.00279053,0.0006014513,0.0002170019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4136076,0.00005395283,0.5844411,0.0004683049,0.0002184977,0.0003049528,0.000001774786,0.00001931344,0.0008844953],"genre_scores_gemma":[0.5758876,0.00001526357,0.4237429,0.0001301361,0.000158574,1.64653e-7,0.0000128348,0.00001383616,0.00003875276],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.503534,"threshold_uncertainty_score":0.5961227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01169434445555908,"score_gpt":0.2147857121726032,"score_spread":0.2030913677170441,"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."}}