{"id":"W3194048014","doi":"10.1080/15481603.2021.1952541","title":"Wetland mapping with multi-temporal sentinel-1 & -2 imagery (2017 – 2020) and LiDAR data in the grassland natural region of alberta","year":2021,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Environment and Protected Areas","funders":"","keywords":"Wetland; Swamp; Marsh; Remote sensing; Environmental science; Random forest; Ground truth; Grassland; Habitat; Topographic Wetness Index; Synthetic aperture radar; Lidar; Geography; Hydrology (agriculture); Ecology; Digital elevation model; Geology","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":[0.0004869983,0.0003948061,0.0001735655,0.001508505,0.0005013652,0.0006995255,0.0005373824,0.0002037072,0.0004370352],"category_scores_gemma":[0.0005073326,0.0001323893,0.0002633171,0.001750942,0.0003006015,0.0002621953,0.0003458172,0.0002326735,0.0001689064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004384749,"about_ca_system_score_gemma":0.003419186,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9024361,"about_ca_topic_score_gemma":0.9435709,"domain_scores_codex":[0.9997486,0.00001593399,0.000009693023,0.00005095465,0.00009688757,0.00007793908],"domain_scores_gemma":[0.9996746,0.00002671416,0.0000324281,0.0000246751,0.0001825641,0.00005911081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009984911,0.0003719542,0.6913705,0.0002342081,0.0002650905,0.001193608,0.0006315712,0.08854654,0.0260258,0.001145682,0.02020127,0.1690152],"study_design_scores_gemma":[0.00005000032,0.00003680342,0.8815543,0.00005099368,0.0000900843,0.0001436964,0.001260538,0.1033849,0.004127481,0.0002031003,0.009055754,0.00004229848],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877967,0.0002410801,0.0006610319,0.0001186093,0.00001750405,0.00002208967,0.008439605,0.0001829272,0.00252031],"genre_scores_gemma":[0.9710768,0.0001974244,0.003487909,0.0000369516,0.00001346468,0.00001141907,0.02347666,0.00001954737,0.001679971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09756392,"threshold_uncertainty_score":0.196277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02983333599267133,"score_gpt":0.2619308661178616,"score_spread":0.2320975301251903,"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."}}