{"id":"W4390880184","doi":"10.3390/fire7010026","title":"Remote Sensing Active Fire Detection Tools Support Growth Reconstruction for Large Boreal Wildfires","year":2024,"lang":"en","type":"article","venue":"Fire","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; Ontario Forest Research Institute; Natural Resources Canada; University of Toronto; Ministry of Natural Resources and Forestry","funders":"U.S. Forest Service; Natural Sciences and Engineering Research Council of Canada; Canadian Forest Service; Ontario Ministry of Natural Resources and Forestry; University of Toronto; University of Alberta","keywords":"Visible Infrared Imaging Radiometer Suite; Environmental science; Boreal; Moderate-resolution imaging spectroradiometer; Taiga; Fire regime; Fire detection; Remote sensing; Kriging; Geospatial analysis; Meteorology; Radiometer; Physical geography; Satellite; Geography; Ecosystem; Ecology; Computer science; Forestry; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001212043,0.000878555,0.0004728574,0.001733526,0.0004998587,0.001127089,0.001331369,0.0005007629,0.001120711],"category_scores_gemma":[0.00400039,0.0005172942,0.0007336868,0.00107734,0.0003011539,0.0008524858,0.0005342173,0.0006522578,0.0003803759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00154942,"about_ca_system_score_gemma":0.00158099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2530639,"about_ca_topic_score_gemma":0.3947781,"domain_scores_codex":[0.9997264,0.00003896063,0.00002093796,0.00009471646,0.00007756941,0.00004144909],"domain_scores_gemma":[0.9987451,0.0004160273,0.0001520901,0.000144639,0.0004592312,0.00008298115],"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":[0.0002750687,0.0003572163,0.2637604,0.0001300523,0.0001863185,0.00024545,0.0004273577,0.5426528,0.00935107,0.00161714,0.004059596,0.1769375],"study_design_scores_gemma":[0.00001105921,0.000007610103,0.02090778,0.00001184889,0.000008623697,0.0000192345,0.00010634,0.9770175,0.0006976567,0.0004721594,0.0007277578,0.00001253472],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.838993,0.0005498595,0.1475762,0.0003242158,0.00006761121,0.0001113688,0.004380479,0.004766396,0.003231027],"genre_scores_gemma":[0.9048814,0.000104918,0.09017508,0.00003056665,0.00001951374,0.00003432852,0.004064685,0.000211691,0.0004777458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2530639,"threshold_uncertainty_score":0.5031818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008821099933384242,"score_gpt":0.224770153648316,"score_spread":0.2159490537149318,"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."}}