{"id":"W2023744534","doi":"10.3390/rs70302431","title":"Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data","year":2015,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Moderate-resolution imaging spectroradiometer; Normalized Difference Vegetation Index; Taiga; Scale (ratio); Vegetation (pathology); Remote sensing; Physical geography; Climatology; Meteorology; Geography; Forestry; Climate change; Cartography; Geology; Satellite","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":[0.0007932278,0.0005532045,0.0003597584,0.001437398,0.0005803168,0.0006398494,0.0007561211,0.0002720781,0.0008881637],"category_scores_gemma":[0.00120919,0.0002456094,0.000318584,0.00117542,0.0001070276,0.0007342859,0.0004144277,0.0003935654,0.0002944738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001820025,"about_ca_system_score_gemma":0.002675419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4176323,"about_ca_topic_score_gemma":0.458281,"domain_scores_codex":[0.9997692,0.00001505443,0.00002035967,0.0000724243,0.0000888317,0.00003424137],"domain_scores_gemma":[0.9994413,0.00004466015,0.00004124213,0.0000492276,0.0003711586,0.00005245762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003684686,0.0004261061,0.2274069,0.0001615375,0.0002154699,0.0001879991,0.0003442861,0.1258217,0.02880651,0.0007662164,0.01437397,0.6011209],"study_design_scores_gemma":[0.00005228276,0.00007820204,0.08785986,0.00002306182,0.00009187268,0.00004652503,0.0002468741,0.8968338,0.008356823,0.0004782887,0.005871989,0.00006042973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.766002,0.0002887213,0.1970992,0.0004864852,0.0001357958,0.0008066505,0.01475161,0.01275886,0.007670663],"genre_scores_gemma":[0.8054737,0.0001267023,0.1829906,0.00006651069,0.00002763608,0.000171747,0.009156117,0.00005789001,0.001928907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4176323,"threshold_uncertainty_score":0.8304029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0799341472034794,"score_gpt":0.2610776516147051,"score_spread":0.1811435044112257,"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."}}