{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001151984,0.00007613754,0.0001011267,0.00003936976,0.0001038991,0.0001373565,0.00009150932,0.00002841277,1.064361e-7],"category_scores_gemma":[0.0002922659,0.00006265735,0.00002829396,0.0001938327,0.00005018176,0.0003338576,0.00009703595,0.00005626039,2.685279e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002018428,"about_ca_system_score_gemma":0.00004792494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003371606,"about_ca_topic_score_gemma":0.000002381227,"domain_scores_codex":[0.9993882,0.00002580418,0.0001385205,0.0002222149,0.0001098845,0.0001154253],"domain_scores_gemma":[0.9990867,0.000241077,0.00008708209,0.0003536002,0.0002113912,0.00002012375],"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":[0.000006710054,0.000004603246,6.516681e-7,0.00001975704,0.000003852924,0.000004930627,0.0002416148,0.002225906,0.06994534,0.001486341,0.00007430197,0.925986],"study_design_scores_gemma":[0.00005391315,0.00001054172,0.00001512364,0.00007990263,0.000004890565,0.00001622796,0.000005691643,0.879179,0.1005218,0.01918902,0.0008620003,0.00006186476],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001217028,0.0002692641,0.9972873,0.0008818951,0.00005309454,0.00009579724,8.678227e-7,0.0001014349,0.00009325759],"genre_scores_gemma":[0.04370897,0.0002181958,0.9554209,0.000518471,0.00003343495,1.42542e-8,0.000001336725,0.00001137243,0.00008731986],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9259241,"threshold_uncertainty_score":0.2555092,"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."}}