Understanding Fire Skips in Juvenile Pine Plantations: Exploring the Relationship Between Fuel Moisture Content and Burn Severity
Bibliographic record
Abstract
ABSTRACT Wildfires are increasing in size and severity due to climate change, and pose a serious threat to humans, infrastructure, and industry. In western North America, wildfires often leave unburned blocks of forest known as fire skips, fire islands, or fire refugia. Research elsewhere has suggested that young forests burn more severely, while mature forest is often found unburned as wildfire refugia. However, preliminary observations in central British Columbia suggest that some planted juvenile stands burn less severely than the surrounding mature forests. The existence of these fire skips could be due to differences in fuel moisture content (FMC), stand structure characteristics that influence fire spread, or both. To identify the role of FMC in fire skips, this study develops relationships between 6116 individual ground‐based FMC measurements and remote sensing data collected in 2021 and 2022. Remotely sensed estimates of pre‐fire FMC were then used to analyse burn severities in different stand types encountered in three major wildfires in central British Columbia in 2017 and 2018. Juvenile plantations, mostly dominated by lodgepole pine ( Pinus contorta var. latifolia ), had lower burn severity than mature and recent cutblocks (open sites) in three case study areas. No significant relationships were found between pre‐fire satellite‐estimated FMC and burn severity in mature forest and juvenile plantations. However, at open stands, moderate correlations between estimated duff FMC and difference normalized burn ratio (dNBR) were found in two case study areas. This work provides tools for assessing pre‐fire FMC at large spatial scales and suggests that the limited fire severity in juvenile stands may be a function of stand characteristics other than pre‐fire FMC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".