{"id":"W3011805300","doi":"10.3390/w12030872","title":"Automatic Surface Water Mapping Using Polarimetric SAR Data for Long-Term Change Detection","year":2020,"lang":"en","type":"article","venue":"Water","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Ministry of Natural Resources and Forestry; York University","funders":"","keywords":"Thresholding; Remote sensing; Synthetic aperture radar; Environmental science; Surface water; Interferometric synthetic aperture radar; Change detection; Polarimetry; Computer science; Geology; Artificial intelligence; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002443891,0.0003137205,0.0001840641,0.001107188,0.0001848956,0.0003475638,0.0002641024,0.0001544305,0.0006318691],"category_scores_gemma":[0.000543181,0.0001523657,0.0001907837,0.001040672,0.0001490097,0.0003598798,0.0002506275,0.000205544,0.0002623556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002221288,"about_ca_system_score_gemma":0.0005645897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01377384,"about_ca_topic_score_gemma":0.02957853,"domain_scores_codex":[0.9998853,0.00002275365,0.000005414289,0.00002722529,0.00004101807,0.00001831667],"domain_scores_gemma":[0.9997918,0.00005381305,0.00003810603,0.00002432062,0.00008249693,0.000009412567],"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":[0.0002193129,0.0001387709,0.03963531,0.0001250095,0.00008635905,0.0002306216,0.0002537254,0.09226608,0.2693203,0.00133475,0.002806207,0.5935836],"study_design_scores_gemma":[0.0000175512,0.00005520783,0.07149351,0.000008267371,0.00003084482,0.00006894342,0.0001150042,0.8970592,0.02846196,0.0008482811,0.001813512,0.00002773765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5132927,0.0002749254,0.479148,0.0001179877,0.00003479045,0.0001369722,0.001190178,0.001995936,0.003808489],"genre_scores_gemma":[0.8541256,0.0001361376,0.1434099,0.00002376403,0.00001302179,0.00006319211,0.001193451,0.00006652036,0.0009684039],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01377384,"threshold_uncertainty_score":0.02738732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.112514680694194,"score_gpt":0.2873842188475054,"score_spread":0.1748695381533115,"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."}}