{"id":"W2976238539","doi":"10.3390/rs11192230","title":"Using Long-Term SAR Backscatter Data to Monitor Post-Fire Vegetation Recovery in Tundra Environment","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Chinese University of Hong Kong; Canadian Space Agency; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Tundra; Environmental science; Backscatter (email); Remote sensing; Vegetation (pathology); Normalized Difference Vegetation Index; Synthetic aperture radar; Arctic; Physical geography; Climate change; Geology; Geography; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003083613,0.0002704162,0.0001429342,0.0005772114,0.0001868492,0.0003207777,0.0001389616,0.0002152263,0.0003523708],"category_scores_gemma":[0.0002555979,0.00007676517,0.0001672717,0.0005195671,0.0001041793,0.0002934904,0.0001469617,0.0001997251,0.0001325959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001377749,"about_ca_system_score_gemma":0.0001197895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004657983,"about_ca_topic_score_gemma":0.01525433,"domain_scores_codex":[0.9998995,0.00001271833,0.00001000586,0.00002880539,0.00002362373,0.00002534882],"domain_scores_gemma":[0.9997988,0.00002153709,0.00008105331,0.00001824358,0.00005687441,0.00002350657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002575697,0.0001682338,0.8670843,0.00008559581,0.0001591047,0.0003621958,0.0003084857,0.003866693,0.08708069,0.00006792063,0.0002691775,0.04029015],"study_design_scores_gemma":[0.000003186151,0.0001314767,0.9847268,0.000009922923,0.00005279354,0.0001562596,0.0003429415,0.006775728,0.007406575,0.00002710171,0.0003569446,0.00001027022],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998615,0.0001192558,0.0007533262,0.000007943619,0.000004360451,0.000003156218,0.000215505,0.00001864784,0.0002627677],"genre_scores_gemma":[0.9977054,0.000133078,0.001342442,0.00001290481,0.000004982259,0.00000462099,0.0005646474,0.000004063502,0.0002279366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004657983,"threshold_uncertainty_score":0.009261727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07583539399714972,"score_gpt":0.2758857142860135,"score_spread":0.2000503202888638,"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."}}