{"id":"W3200733957","doi":"10.1109/jstars.2021.3105645","title":"The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform","year":2021,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Natural Resources Canada; Department of Environment and Conservation, Government of Newfoundland and Labrador; Nature Conservancy of Canada; Nature Conservancy; Environment and Climate Change Canada; National Aeronautics and Space Administration","keywords":"Wetland; Shuttle Radar Topography Mission; Environmental science; Remote sensing; Swamp; Marsh; Scale (ratio); Digital elevation model; Cartography; Geography; Ecology","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":[0.0006663935,0.000152981,0.0002220695,0.00007479345,0.0006484039,0.0000959698,0.0001557795,0.0001570118,0.000005393048],"category_scores_gemma":[0.0002188843,0.0001184766,0.00003304247,0.0006539337,0.0001031315,0.0001606485,0.00008662458,0.0003925344,0.000002283081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002399261,"about_ca_system_score_gemma":0.0001355549,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002787762,"about_ca_topic_score_gemma":0.08630399,"domain_scores_codex":[0.9982971,0.00009775988,0.0006303907,0.000258006,0.0004420016,0.0002747523],"domain_scores_gemma":[0.9987291,0.0001599013,0.0004758169,0.0003288653,0.0002167733,0.00008959942],"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":[0.00006926124,0.00003589687,0.002811487,0.0000268463,0.00005677298,0.00003611399,0.0008105983,0.1682097,0.6703204,0.000111047,0.001608501,0.1559034],"study_design_scores_gemma":[0.0004828309,0.0000372436,0.1221373,0.0001641534,0.00003164355,0.0001355467,0.0001362184,0.8476998,0.01695466,0.0001380395,0.0119097,0.0001728906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924612,0.00008795862,0.005692441,0.0007509985,0.0004293687,0.0001655155,0.0000063661,0.000008289158,0.0003978401],"genre_scores_gemma":[0.8203352,0.0001953686,0.178072,0.0002247397,0.0007029023,1.374203e-8,0.00008191541,0.00002031344,0.0003675446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6794901,"threshold_uncertainty_score":0.9303686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06055230805093607,"score_gpt":0.2441619445346295,"score_spread":0.1836096364836934,"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."}}