Early‐season fires in boreal black spruce forests produce pyrogenic carbon with low intrinsic recalcitrance
Bibliographic record
Abstract
Pyrogenic carbon (PyC), a major by‐product of wildfires in boreal forests, plays several critical roles in soil biogeochemical processes. However, PyC properties, including its potential recalcitrance, may vary depending on its formation conditions. Our study aimed to characterize the chemical and physical properties of PyC formed under variable fire severity in Eastern Canada boreal forests; these latter represent an important fraction of fire‐affected circumboreal ecosystems. A total of 267 PyC samples, produced by early‐season wildfires in 2005–2007, were collected ≤5 years after fire from the forest floors of 14 black spruce sites distributed across Quebec, to cover the range of fire severity encountered in these forests. Early‐season fires occur frequently in Eastern Canada, and are predicted to increase in regional and global scenarios of future fire regimes associated with climate change. Selected PyC samples were analyzed using elemental analysis, solid‐state 13C nuclear magnetic resonance (NMR) spectroscopy, scanning electron microscopy, and surface area analysis. The NMR spectra of the PyC collected on low‐fire‐severity sites were dominated by peaks indicative of cellulose, while those for PyC from higher‐severity sites were dominated by a broad peak assigned to aromatic carbons. Atomic H/C and O/C ratios decreased with increasing fire severity. By comparing field samples to samples produced in the laboratory under controlled formation conditions, we were able to infer that the temperature of formation in the field was low (75°–250°C). In addition, for all PyC samples, the aromatic carbon : total carbon ratio was small, suggesting that PyC produced by early‐season fires in these boreal forests may be susceptible to relatively rapid degradation. Taken together, our data suggest that boreal PyC may not be as recalcitrant as previously assumed, and that its influence on soil biogeochemical processes may be short lived.
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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 | 0.001 | 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".