Short-term impacts of fuel treatments on above-ground forest carbon storage and stability in southeastern British Columbia
Bibliographic record
Abstract
Wildfires produce substantial carbon emissions and are increasingly causing forests to transition from carbon sinks to net carbon sources. Across forests of western North America, legacies of fire suppression and extensive timber extraction have disrupted historical surface fire regimes, resulting in the accumulation of hazardous fuel loads and denser, more homogenous forest landscapes. Fuel treatments are often implemented to proactively reduce the risk of severe wildfire and resulting emissions; however, the effects of these treatments on forest carbon storage and stability are not well characterized in British Columbia, Canada. To better understand the role of carbon in wildfire mitigation efforts, I partnered with five community forests in southeastern British Columbia that implemented different types of fuel treatments between the summers of 2021 and 2022. I estimated differences in above-ground carbon stored on-site before and after treatment and across treatment types while also accounting for the utilization of biomass removed off-site during treatment. I then combined field data and fire effects modeling to quantify potential tree mortality and direct carbon emissions under three future wildfire scenarios in forest stands with and without fuel treatments. Fuel treatments resulted in immediate reductions in carbon storage primarily driven by live tree removals. Compared to pre-treatment conditions, fuel treatments consistently reduced potential tree mortality from wildfire, but they had a minor impact on potential direct carbon emissions. This work develops ecosystem-specific knowledge to critically evaluate the short-term effects of fuel treatments on forest carbon stocks in fire-prone landscapes. Ongoing research is needed to evaluate long-term dynamics of fuel treatments, wildfire, and carbon under climate change.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".