{"id":"W4285253164","doi":"10.1109/jstars.2022.3177579","title":"Wet-GC: A Novel Multimodel Graph Convolutional Approach for Wetland Classification Using Sentinel-1 and 2 Imagery With Limited Training Samples","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Department of Environment and Conservation, Government of Newfoundland and Labrador; European Space Agency","keywords":"Computer science; Wetland; Remote sensing; Convolutional neural network; Synthetic aperture radar; Bottleneck; Artificial intelligence; Graph; Deep learning; Contextual image classification; Multispectral image; Image resolution; Pattern recognition (psychology); Environmental science; Ecology; Image (mathematics); Geology","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":[0.0003884648,0.0001117494,0.0002007306,0.0001198801,0.000410671,0.00005168503,0.00005631644,0.00004622855,0.00000376744],"category_scores_gemma":[0.00001392232,0.00009365866,0.00002488999,0.0004315665,0.00003588389,0.0001320793,0.00002817565,0.0002079222,3.988255e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005612477,"about_ca_system_score_gemma":0.00005668953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001286847,"about_ca_topic_score_gemma":0.0001965221,"domain_scores_codex":[0.9990149,0.0000314348,0.0003456911,0.0001879939,0.0002358155,0.0001841829],"domain_scores_gemma":[0.9994543,0.00008010436,0.0002780635,0.00007086178,0.00006751034,0.00004918602],"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":[0.0004538269,0.0002232626,0.04302569,0.0001983711,0.0001770047,0.000009951895,0.003413476,0.410469,0.5153252,0.0002318392,0.00005203375,0.02642033],"study_design_scores_gemma":[0.001169913,0.00003208466,0.08356897,0.00003184587,0.00003886695,0.0001911839,0.0006360145,0.9134011,0.0001968071,0.0002002995,0.0003990438,0.0001338664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8492565,0.00003302374,0.1502581,0.0001386173,0.00004121133,0.0001841078,0.00001098983,0.000007868289,0.0000696278],"genre_scores_gemma":[0.76962,0.00002425097,0.2301595,0.00008453206,0.00007568033,5.130277e-7,0.00001931233,0.00001003433,0.000006144032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5151284,"threshold_uncertainty_score":0.3819289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0711564252892243,"score_gpt":0.235333452496089,"score_spread":0.1641770272068647,"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."}}