{"id":"W4413478742","doi":"10.1017/eds.2025.10015","title":"Discrete variational autoencoders for synthetic nighttime visible satellite imagery","year":2025,"lang":"en","type":"article","venue":"Environmental Data Science","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Satellite; Remote sensing; Satellite imagery; Computer science; Artificial intelligence; Computer vision; Meteorology; Geography; Physics; Astronomy","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":[0.0003325074,0.0001274547,0.0001010437,0.000129919,0.0002054557,0.00007231079,0.0009395794,0.00002765591,0.0001431006],"category_scores_gemma":[0.00006987889,0.0001264827,0.00002356591,0.0002355103,0.0004267101,0.001445168,0.0004663987,0.00008021334,0.00005755325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001681099,"about_ca_system_score_gemma":0.00003048681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002057971,"about_ca_topic_score_gemma":5.54211e-7,"domain_scores_codex":[0.9988332,0.000007951466,0.0001802303,0.0004438825,0.0002510269,0.0002837255],"domain_scores_gemma":[0.9990401,0.00008689004,0.00002474557,0.0007853427,0.000003803291,0.00005916998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001632352,0.00008044104,0.0005131243,0.00007017511,0.00002484412,0.000003801357,0.0001137505,0.0107238,0.9025307,0.003291973,0.007595447,0.07503562],"study_design_scores_gemma":[0.0003127493,0.00003570914,0.01379737,0.00007615557,0.00002891486,0.000007047228,0.0000932909,0.7171429,0.1753099,0.005674887,0.08701564,0.0005054105],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001433158,0.0003316978,0.9919171,0.0001397189,0.0002687087,0.0003406988,0.0008365053,0.0002909212,0.004441496],"genre_scores_gemma":[0.4543257,0.0005481276,0.5430666,0.0002241628,0.00003947122,0.00008472259,0.0005743346,0.00003285299,0.001104017],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7272208,"threshold_uncertainty_score":0.5157813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008286408837300545,"score_gpt":0.256655688708858,"score_spread":0.2483692798715574,"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."}}