{"id":"W3153361069","doi":"10.3390/rs13081492","title":"Characterizing Wetland Inundation and Vegetation Dynamics in the Arctic Coastal Plain Using Recent Satellite Data and Field Photos","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Resources Conservation Service; Agricultural Research Service; U.S. Fish and Wildlife Service; European Space Agency; U.S. Department of Agriculture; National Aeronautics and Space Administration","keywords":"Wetland; Environmental science; Hydrology (agriculture); Coastal plain; Arctic; Vegetation (pathology); Wildlife refuge; Satellite imagery; Permafrost; Physical geography; Remote sensing; Oceanography; Geology; Wildlife; Ecology; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0002853723,0.0001639466,0.0001236839,0.00125065,0.0002733506,0.0003471899,0.0001576073,0.0001236975,0.0003687002],"category_scores_gemma":[0.0004489019,0.0001039951,0.0002326041,0.001164132,0.0001585214,0.0003143786,0.0002149628,0.0001072927,0.0001084653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003439596,"about_ca_system_score_gemma":0.0003111431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07445057,"about_ca_topic_score_gemma":0.1592918,"domain_scores_codex":[0.9998907,0.00001240386,0.00001217708,0.00004138962,0.00002285885,0.00002047111],"domain_scores_gemma":[0.9997457,0.00003784884,0.0000822955,0.0000218283,0.00008590264,0.00002642063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005357106,0.00004951359,0.9670781,0.00004204913,0.00004613518,0.0001272896,0.00054736,0.004080273,0.004940017,0.00007761447,0.0004643692,0.02249368],"study_design_scores_gemma":[0.000001232237,0.00001030016,0.9916989,0.000008672443,0.00001390078,0.00003663743,0.000504915,0.006745477,0.0004431331,0.00002711805,0.0005037563,0.000006117441],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981687,0.00005243646,0.0004328333,0.00000939925,0.000002401299,0.000006102767,0.0008497514,0.0000269191,0.000451427],"genre_scores_gemma":[0.9951276,0.00009348071,0.002442978,0.000007751933,0.000004989064,0.00001574313,0.002082343,0.000007042052,0.0002181058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07445057,"threshold_uncertainty_score":0.1480345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05918294198557356,"score_gpt":0.2699119759185132,"score_spread":0.2107290339329396,"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."}}