{"id":"W3012469836","doi":"10.3390/asi3010016","title":"Reducing Wooden Structure and Wildland-Urban Interface Fire Disaster Risk through Dynamic Risk Assessment and Management","year":2020,"lang":"en","type":"article","venue":"Applied System Innovation","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Norges Forskningsråd","keywords":"Wildland–urban interface; Geography; Risk assessment; Fire regime; Boreal; Vegetation (pathology); Environmental resource management; Risk management; Climate change; Human settlement; Environmental planning; Environmental protection; Environmental science; Ecology; Ecosystem; Business; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003619218,0.000246486,0.0002682819,0.00003363463,0.0002164193,0.0001298967,0.0001482467,0.00009941973,0.0000323649],"category_scores_gemma":[0.00001006497,0.0002174236,0.00001534997,0.0005480052,0.00006252995,0.0002745583,0.0002782823,0.0002370813,0.00003975569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003741646,"about_ca_system_score_gemma":0.000004214014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004259368,"about_ca_topic_score_gemma":0.00004114545,"domain_scores_codex":[0.9982624,0.0001016496,0.0004577075,0.0006214682,0.0003262829,0.0002304683],"domain_scores_gemma":[0.9991945,0.00004152542,0.0004144087,0.0002850443,0.00001096446,0.000053576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003527838,0.000142546,0.559525,0.005796001,0.0008066645,0.00003511173,0.0352506,0.01309208,0.07622378,0.0129443,0.009083442,0.2867478],"study_design_scores_gemma":[0.003491845,0.0003716724,0.4314421,0.000612325,0.0003046584,0.00004489504,0.006728521,0.5487168,0.001374206,0.0006647536,0.004879617,0.001368548],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833385,0.00004653731,0.01215083,0.0002373552,0.0001611465,0.001216905,0.00004621894,0.0001148865,0.002687625],"genre_scores_gemma":[0.9968954,0.00002361712,0.002730163,0.000121333,0.00005856176,0.00007079994,0.00003459169,0.00003269686,0.00003282922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5356247,"threshold_uncertainty_score":0.8866277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006004902034609243,"score_gpt":0.2260872091481331,"score_spread":0.2200823071135238,"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."}}