What Can Charcoal Reflectance Tell Us About Energy Release in Wildfires and the Properties of Pyrogenic Carbon?
Bibliographic record
Abstract
Here we explore how charcoal formation under different heating regimes and circumstances leads to chars of different physical properties. In order to do this we have undertaken 1) carefully controlled laboratory experiments that replicate the different heating regimes that might be experienced during a wildfire and 2) two experimental wildfires where heat variations were monitored across the burn from which resulting charcoal has been studied. The charcoal properties were assessed using charcoal reflectance that measures the light reflected back from the charcoals structure and which links to changes in its structural properties. We find that increased total heat released during combustion positively correlates with increased charcoal reflectance and that this is evidenced from both our laboratory experiments and experimental wildfires. Charcoals that related to lower total heat release were found to have more lignin remaining than those subjected to greater heating indicating that charcoals formed in lower energy regimes are likely to be more susceptible to post-fire degradation. We conclude that charcoal reflectance may make a useful metric with which to determine the distribution of energy delivery across a burned area and that this may be utilised to inform both variations in fire severity and enable the prediction of long-term C budgeting for different types of wildfire.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".