{"id":"W2142996145","doi":"10.1016/j.rse.2010.02.001","title":"Modeling fire severity in black spruce stands in the Alaskan boreal forest using spectral and non-spectral geospatial data","year":2010,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"U.S. Bureau of Land Management; U.S. Geological Survey; U.S. Fish and Wildlife Service; National Science Foundation","keywords":"Environmental science; Black spruce; Taiga; Boreal; Remote sensing; Vegetation (pathology); Atmospheric sciences; Forestry; Geography; Geology","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":[0.0008861176,0.0006478877,0.0003681591,0.0005525826,0.0004799906,0.0007000723,0.0005468073,0.0004913543,0.0003776766],"category_scores_gemma":[0.001409694,0.0004442025,0.0005960881,0.0004365362,0.0002955691,0.0005937659,0.0002825226,0.0003814626,0.00005400552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00133172,"about_ca_system_score_gemma":0.000772769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1747678,"about_ca_topic_score_gemma":0.2130697,"domain_scores_codex":[0.9998534,0.00003917644,0.00001244844,0.00004679403,0.00001986481,0.00002837281],"domain_scores_gemma":[0.9992695,0.0004053629,0.0000967159,0.00003373485,0.00009338593,0.0001011957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000326525,0.0002041231,0.2269975,0.00001772805,0.0001820479,0.0000927392,0.00008057874,0.7658187,0.001354594,0.0002376073,0.0002220992,0.00446577],"study_design_scores_gemma":[0.00002134815,0.00004366842,0.06754947,0.000002885969,0.00004565726,0.00002171173,0.0001262491,0.9314784,0.0004474918,0.0001862853,0.00006573454,0.00001113112],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993728,0.0000274979,0.0003276292,0.00002199291,0.00000405003,0.000002812148,0.00009840289,0.00001583345,0.0001289705],"genre_scores_gemma":[0.9990916,0.00002107461,0.0005615467,0.000003927689,0.000003021406,0.000003117357,0.0001838529,0.000003199883,0.0001286438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1747678,"threshold_uncertainty_score":0.3475012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01472198332681223,"score_gpt":0.2286485452742916,"score_spread":0.2139265619474794,"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."}}