{"id":"W3196720155","doi":"10.1109/jstars.2021.3110460","title":"Wetland Change Analysis in Alberta, Canada Using Four Decades of Landsat Imagery","year":2021,"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":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Biodiversity Monitoring Institute; University of Alberta; Environment and Climate Change Canada; Canadian Wood Council","funders":"Environment and Climate Change Canada","keywords":"Wetland; Swamp; Shrubland; Grassland; Environmental science; Marsh; Satellite imagery; Climate change; Geography; Hydrology (agriculture); Physical geography; Remote sensing; Ecology; Ecosystem; Geology","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":[0.0001906075,0.00008330541,0.0002850095,0.0001231122,0.0000536054,0.00002199593,0.00005094572,0.00005764943,0.00001575041],"category_scores_gemma":[0.00002408452,0.00007099326,0.00003183012,0.001233507,0.00001017749,0.00009903076,0.00002245762,0.0001462543,1.427986e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009638895,"about_ca_system_score_gemma":0.0001300514,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3206669,"about_ca_topic_score_gemma":0.9550466,"domain_scores_codex":[0.9990647,0.00003997104,0.0004129571,0.0001174412,0.0002101122,0.0001548485],"domain_scores_gemma":[0.999508,0.00007809745,0.0002151771,0.00008803148,0.00006222846,0.00004852558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003419473,0.00003222551,0.9060222,0.00009098229,0.0001855735,0.0001895579,0.001004574,0.04085767,0.03338166,0.000008116884,0.00002291206,0.01817032],"study_design_scores_gemma":[0.0004700737,0.00001030222,0.8276159,0.0001300484,0.0001109331,0.00007346693,0.0001558201,0.1644985,0.006112917,0.0001416914,0.0005443747,0.0001359761],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989676,0.00007076895,0.0003867829,0.0002703906,0.00007779714,0.00005001367,0.000002230729,0.0000012965,0.0001731305],"genre_scores_gemma":[0.9892029,0.0001481213,0.01043942,0.0001085854,0.00007855605,2.90045e-8,0.000003122523,0.000005077356,0.00001415968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6343797,"threshold_uncertainty_score":0.6838568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02830085819399049,"score_gpt":0.2214595760025061,"score_spread":0.1931587178085156,"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."}}