https://w3id.org/np/RAei1P_vxhCMfTikg-uVjuj_nnG4gmeu8cQ9nazvQKXUM
has assertion: assertion
has provenance: provenance
has publication info: pubinfo
type: Nanopublication
created: 2026-07-08T15:44:42.565Z
creator: Emily Regalado
license: https://creativecommons.org/licenses/by/4.0/
introduces: Learning from Wildfire Decision Support: large language model analysis of barriers to fire spread in a census of large wildfires in the United States (2011–2023)
was created at: nanodash.knowledgepixels.com

Learning from Wildfire Decision Support: large language model analysis of barriers to fire spread in a census of large wildfires in the United States (2011–2023)
creator: https://orcid.org/0000-0001-7818-1559
publisher: 051hpv692
subject: NCIT C16309
type: article
type: FAIRDigitalObject
comment: Background Barriers are the landscape features that firefighters leverage to stop wildfire spread. In the United States, decision-makers discuss barrier availability in a framework called the Wildland Fire Decision Support System (WFDSS). Aims This study analyzes WFDSS text from 6630 large wildfires and examines the barriers identified. Methods A large language model was trained and validated, then used to detect 13 different barriers. Burn scar and fuel treatment barriers were compared with their availability near each fire. Key results Decision-makers recognize barriers on most wildfires (75%) and explicitly state when they are not present. Roads (42% of incidents), burn scars (26%) and natural fuel variability (25%) are the most common barriers. There is a strong relationship between the amount of burned area and the probability that it is identified as a barrier. The relationship between fuel treatment barriers and their availability is weak, but fuel treatments are recognized as barriers at lower landscape thresholds than burn scars. Conclusions Prior wildfire yields more opportunities for stopping fire spread than fuel treatments. However, a smaller area must be treated than burned naturally before fire managers consider it a barrier. Implications This study helps direct policy towards expanding useful barriers.
funder: 021nxhr62
end date: 04 September 2025
start date: 2024

sig
has algorithm: RSA
signed by: Emily Regalado