{"id":"W3205209293","doi":"10.1109/tgrs.2021.3113856","title":"WetNet: A Spatial–Temporal Ensemble Deep Learning Model for Wetland Classification Using Sentinel-1 and Sentinel-2","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Natural Resources Canada","keywords":"Computer science; Artificial intelligence; Deep learning; Land cover; Boosting (machine learning); Synthetic aperture radar; Machine learning; Remote sensing; Data mining; Pattern recognition (psychology); Land use; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002524692,0.0001559084,0.0001451402,0.00007100047,0.0007814191,0.0001347648,0.00004888178,0.00006213177,0.00001157683],"category_scores_gemma":[0.000006601781,0.0001496606,0.00005230254,0.0002430505,0.0001643244,0.0002405267,0.000009764414,0.0001434956,0.000004074975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005291752,"about_ca_system_score_gemma":0.00002136113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006448561,"about_ca_topic_score_gemma":0.0009837479,"domain_scores_codex":[0.9987161,0.00004788329,0.0001951771,0.0005069867,0.0002326219,0.0003012863],"domain_scores_gemma":[0.9996192,0.00004085605,0.00008334001,0.0001474827,0.00002325104,0.00008591328],"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":[0.0000378061,0.00007351221,0.0003875884,0.00004839625,0.00002192872,0.00001468536,0.00082915,0.08625526,0.228304,0.000008392576,0.00002861471,0.6839906],"study_design_scores_gemma":[0.0003801369,0.00002167946,0.001271573,0.00003519544,0.00005732153,0.00003253103,0.0003182175,0.9921476,0.005011716,0.0001644123,0.0003705214,0.0001891178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.299845,0.0000164445,0.6993547,0.0002778633,0.0001095796,0.0001376998,9.748571e-7,0.00003018719,0.000227526],"genre_scores_gemma":[0.9104782,0.0001777798,0.08785388,0.0001746427,0.00002379301,4.262116e-7,0.000003284918,0.00001490187,0.001273105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9058923,"threshold_uncertainty_score":0.6102982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02679618119896897,"score_gpt":0.2618678697287695,"score_spread":0.2350716885298005,"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."}}