{"id":"W2152287417","doi":"10.1111/j.1365-2486.2009.01956.x","title":"Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets","year":2009,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada; Canadian Space Agency; National Oceanic and Atmospheric Administration; Universidad de Málaga","keywords":"Normalized Difference Vegetation Index; Environmental science; Boreal; Taiga; Vegetation (pathology); Physical geography; Satellite; Satellite imagery; Moderate-resolution imaging spectroradiometer; Climatology; Remote sensing; Geography; Climate change; Forestry; Ecology","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":[0.0002454865,0.0001573587,0.0002510533,0.000010962,0.0001689103,0.0000140325,0.0006962199,0.00004871032,0.000009750549],"category_scores_gemma":[0.00007606801,0.00008394463,0.00005799944,0.0004378392,0.0004147785,0.00007840464,0.0002894358,0.000113307,0.00002289117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008532316,"about_ca_system_score_gemma":0.000005124062,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0182477,"about_ca_topic_score_gemma":0.01822941,"domain_scores_codex":[0.9986714,0.0002623531,0.0002294071,0.000281276,0.000163734,0.0003918568],"domain_scores_gemma":[0.9991457,0.0001455556,0.0002751026,0.0003700893,0.000005089381,0.00005842844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006703329,0.000006688556,0.8219087,0.000002519256,0.000007332111,7.31035e-7,0.0001298987,5.870236e-7,0.00002446,0.00002949396,0.00008639499,0.1777965],"study_design_scores_gemma":[0.00007881592,0.0002616941,0.9977313,0.00001495313,0.00001531191,0.000005839089,0.00002932313,0.0002633217,0.00001591559,0.0001083413,0.001374291,0.0001008456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977054,0.0001068565,0.00005650894,0.0006283749,0.000123412,0.0002996398,0.0003723233,0.00002620078,0.0006813109],"genre_scores_gemma":[0.9990757,0.00001776221,0.0001222959,0.0004779875,0.0001730112,0.0000126269,0.000114042,0.000004302504,0.000002254279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1776957,"threshold_uncertainty_score":0.9996853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02575766975860384,"score_gpt":0.2565823372461281,"score_spread":0.2308246674875243,"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."}}