Estimating consumption and remaining carbon in burned slash piles
Bibliographic record
Abstract
Fuel reduction treatments to reduce fire risk have become commonplace in the fire adapted forests of western North America. These treatments generate significant woody debris, or slash, and burning this material in piles is a common and inexpensive approach to reducing fuel loads. Although slash pile burning is a common practice, there is little information on consumption or even a common methodology for estimating consumption. As considerations of carbon storage and emissions from forests increase, better means of quantifying burn piles are necessary. This study uses two methods, sector sampling and a form of line intersect sampling, for estimating both the percent consumption and conversion to charcoal in slash piles of ponderosa pine ( Pinus ponderosa Douglas ex P. Lawson & C. Lawson) in northern Arizona, USA. On average, burning released between 92% and 94% of the carbon in each slash pile to the atmosphere and converted between 0.05 and 0.34 Mg C·ha–1 to charcoal across the treatment area. These results demonstrate that burning slash piles converts significant quantities of carbon stored in wood to atmospheric carbon and charcoal, both of which should be considered as forest carbon accounting is further refined. Sector sampling and line intersect strategies produced similar estimates of consumption; however, the line intersect protocol was more easily and rapidly implemented.
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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 | 0.000 | 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".