{"id":"W4412531504","doi":"10.1016/j.engappai.2025.111706","title":"Super-resolution reconstruction of WorldView-3 multispectral satellite images based on generative adversarial networks","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ontario Ministry of Natural Resources and Forestry","keywords":"Computer science; Multispectral image; Adversarial system; Generative grammar; Artificial intelligence; Satellite; Superresolution; Generative adversarial network; Computer vision; Resolution (logic); Remote sensing; Image (mathematics); Pattern recognition (psychology); Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000223747,0.0001488145,0.0001938827,0.0003391752,0.00008257539,0.0000376879,0.0005308629,0.00006961705,0.000003594643],"category_scores_gemma":[0.0001340402,0.0001651502,0.00007073769,0.001093418,0.0001228002,0.0002545446,0.00006535038,0.0001785838,0.000002653957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007531733,"about_ca_system_score_gemma":0.00006523736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001833802,"about_ca_topic_score_gemma":0.000003553508,"domain_scores_codex":[0.9988207,0.00002719292,0.0004867076,0.0003412447,0.0001427003,0.0001814426],"domain_scores_gemma":[0.9988374,0.0002194322,0.0001519532,0.0005268307,0.000230911,0.00003343943],"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":[0.0000174482,0.00008778832,0.00002063969,0.00003963075,0.00001036216,2.382787e-7,0.00006241237,0.4395706,0.04172671,0.07179779,0.00002070308,0.4466457],"study_design_scores_gemma":[0.00001558932,0.00002704018,0.00004141609,0.00008469087,0.000005290893,5.364221e-7,0.0000104559,0.6596048,0.33643,0.003549197,0.0001477111,0.00008334661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001850609,0.0003116347,0.9983072,0.0002156993,0.0001834698,0.0003405302,0.00000482134,0.0002442326,0.0002073608],"genre_scores_gemma":[0.4348963,0.00004183739,0.5648988,0.00001492673,0.00003680485,0.0000911857,0.000003290173,0.000006008195,0.00001083825],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4465623,"threshold_uncertainty_score":0.673463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121973530053961,"score_gpt":0.2723580835264879,"score_spread":0.2611383482259483,"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."}}