Bibliographic record
Abstract
Wildfire is the primary disturbance mechanism in much of the boreal forest, initiating succession and influencing ecosystem processes. While most organic matter is consumed, a small amount is converted to charcoal. Fire derived charcoal is highly resistant to mineralization, acting as a recalcitrant C pool, as well as contributing to ecosystem function through nutrient retention and nitrification. In this study we examined the charcoal content of soils across a 14–208‐year post‐fire chronosequence in the southern boreal forest of North America, and compared wildfire derived charcoal to laboratory produced charcoal. Charcoal stocks averaged 4.09 Mg ha−1 representing 2.19 Mg C ha−1; 8–10% of soil C in typical boreal soils. NMR results showed charcoal to be primarily aryl C with smaller amounts of alkyl‐C, O‐alkyl, and methoxyl groups. Charcoal stocks changed substantially over time with C content declining from 56% to 46% and a loss of more labile C groups. Charcoal from stands greater than 140 years old was almost entirely aryl C. Laboratory produced charcoal between 300 and 350°C was found to have comparable properties to wildfire charcoal. Charcoal C stocks in our study are 2–3 times greater than those reported for Eurasian boreal ecosystems, where non‐stand replacing fire is the predominant disturbance; suggesting global estimates of boreal charcoal C stocks may need to be revised upwards to account for greater wildfire intensity in North American boreal forests. Boreal charcoal stocks in this study were found to represent an important long‐term soil C stock, large enough to affect soil processes. Changes in C content and chemical composition, however, occur over time, especially in the first ∼100 years. In the absence of fire, management of boreal charcoal stocks may be required to maintain ecosystem function and C balance.
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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 | 0.001 | 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".