{"id":"W2202623230","doi":"10.5194/bg-13-389-2016","title":"Vegetation structure and fire weather influence variation in burn severity and fuel consumption during peatland wildfires","year":2016,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Manchester; Natural Environment Research Council; Sight Research UK","keywords":"Environmental science; Moorland; Peat; Vegetation (pathology); Prescribed burn; Fire regime; Atmospheric sciences; Flammability; Vegetation type; Hydrology (agriculture); Physical geography; Ecology; Forestry; Ecosystem; Geography; Shrub; Geology; Medicine","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":[0.0005134642,0.0002036876,0.0002360102,0.0006486409,0.0003036356,0.0005082146,0.0002024517,0.0001791902,0.001104391],"category_scores_gemma":[0.001072624,0.0001626945,0.0002462206,0.0006321188,0.0002979147,0.0002626607,0.0002452114,0.0002059866,0.0001647059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008123164,"about_ca_system_score_gemma":0.0003186812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1425885,"about_ca_topic_score_gemma":0.411422,"domain_scores_codex":[0.999757,0.00005970617,0.00001626834,0.00006050949,0.00004894711,0.00005745124],"domain_scores_gemma":[0.9991066,0.0002169537,0.0002906303,0.0000567104,0.0001787745,0.0001502958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001447885,0.0000203566,0.9940818,0.00001308724,0.00006194105,0.00002621774,0.0003022356,0.0002466503,0.002028831,0.00001302261,0.0000622649,0.002998929],"study_design_scores_gemma":[1.761556e-7,0.000004420038,0.9998206,0.000001102551,0.000001890926,0.000005845445,0.00004939436,0.00007077986,0.00002395892,0.000002099599,0.00001923153,6.595118e-7],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994965,0.00005665603,0.00007758975,0.000003384882,0.000001044608,0.000001855624,0.0001853335,0.00000249856,0.0001751673],"genre_scores_gemma":[0.9994441,0.00002379642,0.00009778106,0.000002697677,9.148402e-7,0.000002573847,0.000239444,0.00000226434,0.000186406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1425885,"threshold_uncertainty_score":0.2835171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004629608805670047,"score_gpt":0.1978940679371273,"score_spread":0.1932644591314572,"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."}}