{"id":"W2109421115","doi":"10.1016/j.rse.2006.01.011","title":"Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada","year":2006,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":247,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; National Aeronautics and Space Administration","keywords":"Normalized Difference Vegetation Index; Environmental science; Advanced very-high-resolution radiometer; Biome; Disturbance (geology); Boreal; Vegetation (pathology); Land cover; Ecoregion; Fire regime; Physical geography; Satellite imagery; Taiga; Remote sensing; Ecological succession; Biogeochemical cycle; Satellite; Climatology; Ecosystem; Land use; Climate change; Geography; Forestry; Ecology; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007164061,0.0003318157,0.0002765407,0.002460649,0.001236279,0.001213501,0.0006356451,0.000325082,0.00064738],"category_scores_gemma":[0.003263847,0.0002763241,0.0003724692,0.00515528,0.0004812415,0.0004271163,0.0004221331,0.0004138919,0.0001090858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01617744,"about_ca_system_score_gemma":0.01228764,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9962333,"about_ca_topic_score_gemma":0.997707,"domain_scores_codex":[0.999627,0.0000324252,0.00002465344,0.0000749138,0.0001375141,0.0001034287],"domain_scores_gemma":[0.9978207,0.0003677162,0.0002955649,0.0001002984,0.001182189,0.00023348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001966389,0.0001255969,0.953891,0.00004571008,0.0003455787,0.000167315,0.0006622053,0.01077469,0.001596521,0.0002895829,0.002229073,0.02967612],"study_design_scores_gemma":[0.00001868281,0.00001244009,0.9858007,0.00001564697,0.00007033034,0.00002551083,0.0009638107,0.01107217,0.0003279665,0.00007162521,0.001606617,0.00001461155],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962413,0.0002125314,0.0002780137,0.0001000918,0.000005757743,0.00001730668,0.002255966,0.00002853577,0.0008605964],"genre_scores_gemma":[0.9940227,0.0002626324,0.001031318,0.00003383075,0.000004287288,0.00001414719,0.003759743,0.00001166264,0.0008597163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01617744,"threshold_uncertainty_score":0.1173761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01050507456909123,"score_gpt":0.2185109084155379,"score_spread":0.2080058338464466,"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."}}