{"id":"W2623913549","doi":"10.3390/rs9060596","title":"Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":330,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Panchromatic film; Sharpening; Multispectral image; Remote sensing; Spectral bands; Image resolution; Environmental science; Geology; Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002534707,0.0003040553,0.0004062219,0.0001274414,0.0004705972,0.0001165824,0.0002179092,0.0001297824,0.00009165165],"category_scores_gemma":[0.0001489757,0.0002966747,0.0001408071,0.00004775715,0.0001756931,0.0004548719,0.00027395,0.0002393257,0.00007544659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001764324,"about_ca_system_score_gemma":0.00000970262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000164171,"about_ca_topic_score_gemma":0.00001322619,"domain_scores_codex":[0.9983965,0.00004843729,0.0004524414,0.0003615549,0.000254367,0.0004866858],"domain_scores_gemma":[0.9985588,0.00008780427,0.0001908999,0.0009292925,0.0001560147,0.00007719202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002240573,0.00000328295,0.00009217329,0.0000895252,0.00001916134,0.00005590688,0.0002689873,0.001952602,0.990479,1.531551e-7,0.001092833,0.005923952],"study_design_scores_gemma":[0.000177878,0.000003985766,0.0003418274,0.000234663,0.00001179361,0.0000063227,0.00001848768,0.4318939,0.566229,0.0001055948,0.0007658382,0.0002107553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5520071,0.0001647739,0.443944,0.00009943346,0.000199037,0.000143321,0.00001013339,0.0006479751,0.00278417],"genre_scores_gemma":[0.7023014,0.00001767176,0.2969404,0.00002260405,0.0001261894,3.397374e-8,0.00004243838,0.00008618837,0.0004631386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4299413,"threshold_uncertainty_score":0.9999486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334474814316472,"score_gpt":0.2351637131616306,"score_spread":0.2218189650184659,"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."}}