{"id":"W4402508004","doi":"10.1016/j.eja.2024.127333","title":"SSGAN: Cloud removal in satellite images using spatiospectral generative adversarial network","year":2024,"lang":"en","type":"article","venue":"European Journal of Agronomy","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Satellite; Generative grammar; Adversarial system; Cloud computing; Generative adversarial network; Satellite image; Computer science; Environmental science; Remote sensing; Artificial intelligence; Image (mathematics); Geology; Engineering","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":[0.0007280945,0.0009612133,0.0005267562,0.0003732175,0.0002611562,0.0004341084,0.0008051762,0.0006848022,0.0009160977],"category_scores_gemma":[0.001305982,0.0002837078,0.0006829974,0.0002835717,0.0006516411,0.0007060186,0.0009562635,0.0009458732,0.0002143222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006630935,"about_ca_system_score_gemma":0.000515731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004706911,"about_ca_topic_score_gemma":0.004947409,"domain_scores_codex":[0.9996955,0.0001013656,0.00001009833,0.00006063505,0.00008174915,0.00005055543],"domain_scores_gemma":[0.9994678,0.0003140629,0.00006698982,0.00004963026,0.00007838233,0.00002313694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008642047,0.00002750325,0.0008896475,0.00003898832,0.00005363549,0.00009451561,0.00003623612,0.9540485,0.00411461,0.003406353,0.0009277088,0.03627576],"study_design_scores_gemma":[0.000002077447,0.00001550502,0.00009871485,0.000003550609,0.00000587756,0.00001972618,0.000003930792,0.9978547,0.0008798345,0.0009457028,0.0001669722,0.000003514019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06394973,0.0008266938,0.9299252,0.0005676835,0.0001241381,0.00006130763,0.0001119176,0.0006116044,0.003821721],"genre_scores_gemma":[0.9256986,0.000456536,0.06892392,0.0004132577,0.00006692388,0.00007626673,0.0002569908,0.00006725273,0.004040251],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004706911,"threshold_uncertainty_score":0.009359062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01082444339609193,"score_gpt":0.2287938467038165,"score_spread":0.2179694033077245,"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."}}