{"id":"W4400086229","doi":"10.3390/rs16132352","title":"Reconstructing Snow-Free Sentinel-2 Satellite Imagery: A Generative Adversarial Network (GAN) Approach","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Remote sensing; Satellite; Generative grammar; Snow; Adversarial system; Generative adversarial network; Satellite imagery; Environmental science; Computer science; Geology; Meteorology; Artificial intelligence; Deep learning; Geography; Astronomy; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003083294,0.0003385317,0.0003363827,0.0001558542,0.0001552485,0.0002140541,0.0001504456,0.0001508228,0.00001652126],"category_scores_gemma":[0.0001585371,0.0003570026,0.0001355051,0.0005797496,0.00008104019,0.0003395995,0.0001150363,0.0005594382,0.00003597014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001673,"about_ca_system_score_gemma":0.00003433457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009610962,"about_ca_topic_score_gemma":0.00000225669,"domain_scores_codex":[0.9982793,0.00007275862,0.0004092052,0.0004665376,0.0002115408,0.0005606021],"domain_scores_gemma":[0.9991122,0.0001724562,0.00004907176,0.0005040047,0.00006619683,0.00009600169],"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":[0.00001213486,0.000002860907,0.000003371064,0.0001754942,0.0001078985,0.0002170967,0.0005951689,0.02210688,0.09210313,0.0001338545,0.004937401,0.8796047],"study_design_scores_gemma":[0.0001536721,0.000006527821,0.000002199309,0.0004148377,0.00003680624,0.0003686436,0.0001244465,0.9488515,0.03810041,0.004037241,0.00750389,0.0003998176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008914381,0.002295807,0.935213,0.00007804715,0.001585819,0.0002782918,0.000005759089,0.003547307,0.04808161],"genre_scores_gemma":[0.08429219,0.0004839884,0.9121514,0.0000847859,0.002450676,1.111181e-7,0.0000306473,0.0001785201,0.0003276916],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9267446,"threshold_uncertainty_score":0.9998882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01079640871449173,"score_gpt":0.2273485692592623,"score_spread":0.2165521605447706,"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."}}