{"id":"W4376130727","doi":"10.1016/j.jag.2023.103333","title":"WetMapFormer: A unified deep CNN and vision transformer for complex wetland mapping","year":2023,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Convolutional neural network; Wetland; Artificial intelligence; Deep learning; Computer science; Environmental science; Geography; Remote sensing; Cartography; Ecology","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":[0.0004795984,0.001325573,0.0004388525,0.0007015233,0.0002991226,0.0006038506,0.002223988,0.0007513121,0.004252606],"category_scores_gemma":[0.0008855922,0.0004723609,0.0005974994,0.0005209873,0.0003081077,0.001676531,0.001269822,0.001042467,0.001145343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009323623,"about_ca_system_score_gemma":0.00139519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01226342,"about_ca_topic_score_gemma":0.02507546,"domain_scores_codex":[0.9998327,0.00001208061,0.000007519124,0.00005775698,0.00005347641,0.00003635836],"domain_scores_gemma":[0.9998299,0.00002836911,0.00001743524,0.00003233804,0.00007127833,0.00002070162],"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":[0.0002222424,0.0002379955,0.003692353,0.000133893,0.0001791246,0.0001680496,0.00006285617,0.16288,0.02997968,0.004892052,0.02205773,0.775494],"study_design_scores_gemma":[0.00002636306,0.0001116775,0.0008927934,0.000009121611,0.00002639194,0.00009251115,0.00002128156,0.9813731,0.01189234,0.001825752,0.003714354,0.00001428488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05792957,0.0005432938,0.9201475,0.0003754582,0.0002263117,0.000324569,0.0009359631,0.01262083,0.006896352],"genre_scores_gemma":[0.4942502,0.0004965038,0.4828793,0.0009001315,0.00008053739,0.0003682077,0.003983965,0.0004213665,0.0166198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01226342,"threshold_uncertainty_score":0.02438408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02214699711599766,"score_gpt":0.2703776256412099,"score_spread":0.2482306285252122,"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."}}