How does increased fire frequency affect carbon loss from fire? A case study in the northern boreal forest
Bibliographic record
Abstract
Fire frequency is expected to increase due to climate warming in many areas, particularly the boreal forests. An increase in fire frequency may have important effects on the global carbon cycle by decreasing the size of boreal carbon stores. Our objective was to quantify and compare the amount of carbon consumed during and the amount of carbon remaining following fire in black spruce (Picea mariana (Mill.) BSP) forests burned after long v. short intervals. We hypothesised that stands with a shortened fire return interval would have a higher carbon consumption than those experiencing a historically typical fire return interval. Using field measurements of forest canopy, soil organic horizons and adventitious roots, we reconstructed pre-fire stand conditions to estimate the biomass lost in each fire and the effects on post-fire residual carbon stores. We found evidence of a higher loss of carbon following two fire events that recurred after a short interval, resulting in a much greater total reduction in carbon relative to pre-fire or mature stand conditions. Consequently, carbon storage across disturbance intervals was dramatically reduced following short-interval burns. Recovery of these stores would require a subsequent lengthening of the fire cycle, which appears unlikely under future climate scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".