Differences in the intensity of past forest fire events inferred from stable oxygen isotope analysis of charred bark
Bibliographic record
Abstract
Understanding past fire regimes requires reliable proxy data that record fire conditions and preserve them over time. The objective of this study was to determine if the oxygen isotope composition of charred bark samples (pyrogenic organic matter) could be used as proxy data to differentiate wildfires based on burn intensity. We collected charred and uncharred bark samples from three fire sites in northern Ontario, Canada that represented a gradient of fire intensity as depicted by Fire Weather Index (FWI) data. We hypothesized that the mean Δ18Obark-char (the difference between δ18O of uncharred bark and a charred sample) would be greater for fires with higher intensities. Analysis of variance of Δ18Obark-char indicated a significant effect of fire event ( F = 73.6, p < 0.001), which explained 57.0% of the variance. A prescribed surface fire treatment (mean FWI = 9.5) had significantly lower Δ18Obark-char than two natural crown fires (FWI = 21 and 27). These results demonstrate that Δ18Obark-char differentiated moderate from high intensity fires in a similar manner to the FWI data.
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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) | 0.000 | 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".