Deeper burning in a boreal fen peatland 1‐year post‐wildfire accelerates recovery trajectory of carbon dioxide uptake
Bibliographic record
Abstract
Abstract Peatlands contain a globally significant store (30%) of soil carbon (C). Within the Canadian Western Boreal Plains, where peatlands are a dominant feature, the climate is becoming warmer and drier, coupled with an increase in forest fire incidence. The response of peatlands to forest fire is likely to be a key determinant in the future of C storage of Boreal peatlands. This study examined the impacts of fire on key environmental controls on CO2 fluxes at the plot‐scale (using static chambers) between burned and unburned understory vegetation throughout the growing season of 2017 in a treed fen impacted by the Horse River wildfire (2016) in Fort McMurray, Alberta, Canada. Both gross ecosystem productivity (GEP) and total respiration (Rtot) were less at burned plots compared with unburned. Temporal patterns varied between the plots, where both component of CO2 fluxes at the unburned plots were largest in June, whereas at the burned plots, CO2 fluxes peaked in the late growing season. GEP and net ecosystem exchange (NEE) showed a positive relationship with depth of burn, with the deepest burned areas showing significantly greater CO2 uptake, coinciding with both increased bioavailable phosphorus and greater moss recolonization. At the unburned plots, soil temperature was a dominant control on CO2 fluxes. This work demonstrates the importance of the depth of burn to post‐fire carbon fluxes and how a knowledge of burn severity and depth can inform understanding of the recovery trajectory of northern peatlands following fire disturbance.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".