{"id":"W3207388182","doi":"10.3390/rs13204044","title":"Model Specialization for the Use of ESRGAN on Satellite and Airborne Imagery","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; University of Arizona","keywords":"Hallucinating; Computer science; Generalization; Artificial intelligence; Generative grammar; Satellite imagery; Remote sensing; Mathematics; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004624964,0.0004839441,0.0002163556,0.0002226453,0.0001698332,0.0003823692,0.0006112448,0.0005603902,0.004370666],"category_scores_gemma":[0.0009271221,0.0002276466,0.0006288274,0.0002075644,0.0003936767,0.0006483785,0.001107767,0.001260867,0.001252725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000289496,"about_ca_system_score_gemma":0.0004073711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002071499,"about_ca_topic_score_gemma":0.004791771,"domain_scores_codex":[0.9998695,0.00003217332,0.00000635819,0.00004589594,0.00002663882,0.0000195191],"domain_scores_gemma":[0.999816,0.00005815068,0.0000139896,0.00007096012,0.00003111766,0.000009852593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002214864,0.0001329265,0.002772197,0.0001864725,0.000160099,0.0005142321,0.0001473762,0.6844259,0.05681656,0.05746599,0.01065633,0.1865004],"study_design_scores_gemma":[0.000007500284,0.00002905024,0.0005965255,0.00001724311,0.00001132318,0.0001581393,0.00001169422,0.973021,0.009046091,0.0120033,0.005086895,0.00001115061],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03853501,0.0003240845,0.9481189,0.0007314499,0.0001018274,0.00006409072,0.0003819272,0.001850788,0.009891941],"genre_scores_gemma":[0.7223276,0.0004621734,0.2624782,0.0004767832,0.00006116048,0.0001998126,0.001249123,0.0004301318,0.01231501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004370666,"threshold_uncertainty_score":0.01462132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08002190815484558,"score_gpt":0.2966182921911709,"score_spread":0.2165963840363253,"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."}}