{"id":"W4322503029","doi":"10.3390/rs15051253","title":"Water Body Extraction from Sentinel-2 Imagery with Deep Convolutional Networks and Pixelwise Category Transplantation","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Multispectral image; Pattern recognition (psychology); Segmentation; RGB color model; Field (mathematics); Remote sensing; Geology; Mathematics","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":[0.0001453554,0.0001101805,0.0000860829,0.00004054517,0.0001676302,0.00004830881,0.00002927187,0.00004105035,0.00008096977],"category_scores_gemma":[0.000001451066,0.00008430542,0.00002117604,0.0001095903,0.00007296653,0.0002277135,0.00003998672,0.00009208824,0.0001393722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004980425,"about_ca_system_score_gemma":0.000002681727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007806348,"about_ca_topic_score_gemma":0.0002392479,"domain_scores_codex":[0.9991433,0.00004052816,0.0001187714,0.000262815,0.0001990748,0.0002355181],"domain_scores_gemma":[0.9997754,0.00003477607,0.00003575696,0.00009803793,0.000005658921,0.00005040439],"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":[0.0004031033,0.00006578267,0.01430079,0.00007530118,0.0001871206,0.00100272,0.002207325,0.2362547,0.5691456,0.00003937856,0.003129868,0.1731883],"study_design_scores_gemma":[0.0003879255,0.00001277112,0.0794397,0.00002049728,0.00006767005,0.00002549558,0.0001452099,0.9168266,0.00212905,0.0003057752,0.0004782173,0.0001610886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8360873,0.0000180418,0.1618504,0.0002480662,0.000123337,0.0001155834,7.440983e-7,0.00009690278,0.001459636],"genre_scores_gemma":[0.9966359,0.0002267969,0.002427994,0.00009201734,0.00009004104,6.372583e-8,0.0001692529,0.00001562434,0.0003423034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6805719,"threshold_uncertainty_score":0.3437876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006771845960404652,"score_gpt":0.2167335015733782,"score_spread":0.2099616556129736,"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."}}