Oxygen isotope values of charred tree bark as an indicator of forest fire severity
Bibliographic record
Abstract
• Charred tree bark is a useful source of information about fire characteristics. • Oxygen isotope signature of charred bark is a linearly related to burn severity. • These δ 18 O values could be used to reconstruct the severity of fire events. • This technique could extend the temporal coverage of fire reconstruction. The objective of this study was to determine if oxygen isotope values of charred tree bark could be used to reconstruct fire severity. The study was completed north of River Valley, Ontario, Canada, where a wildfire burned approximately 2500 hectares of white pine ( Pinus strobus L.) forest in 2018. We established a network of field plots, collected charred bark samples from standing white pine stems, and estimated burn severity based on a standard field assessment protocol known as the Composite Burn Index (CBI). We also analyzed pre- and post-fire Sentinel-2 imagery of the burn area to compute various Normalized Burn Ratio (NBR)-based change detection algorithms, which are known to produce reliable predictions of CBI. We developed simple linear regression models to predict CBI using either the δ 18 O values of charred bark or versions of the NBR. Models developed from the δ 18O values of charred bark revealed a significant negative relationship between CBI and plot-level δ 18 O, with the strongest relationship being with maximum δ 18 O (r 2 = 0.179, RMSE = 0.565). There were significant positive relationships between all NBR indices and CBI, with better fit statistics than the δ 18 O models. The results demonstrate that δ 18 O can be used as a predictor of fire severity; however, the scale of measurement of fire severity is finer (tree-level) than the plot-level CBI and NBR indices. The advantage of using the δ 18 O method is that it can be used to reconstruct fire severity when satellite or field data are unavailable.
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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 | 0.000 |
| 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) | 0.000 | 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".