Wildfire, Degradation and Climate Change: A Triple Threat for the Northern Peatland Carbon Sink
Bibliographic record
Abstract
The northern peatland carbon sink is critical for the regulation of the Earth’s climate, however, it is experiencing increasing stressors due to both anthropogenic and climate-mediated disturbances. This talk will discuss the impact of compounding disturbances on northern peatlands and present a large-scale modelling effort to quantify the effect on medium-term (100-yr) carbon dynamics. Direct, anthropogenic disturbance such as peatland drainage for horticultural, agricultural, forestry or development purposes, disrupts the ecohydrological feedbacks that promote the resilience of peatlands to other disturbances. Climate change stressors such as long-term drying and increased severity of drought can have similar or compounding effects. When degraded ecosystems are impacted by wildfire they tend to burn much more severely than their pristine counterparts, releasing around ten times more carbon into the atmosphere. There is considerable spatial variability in carbon losses due to variation in peat properties and ecohydrological conditions. Further, there is limited understanding of the post-fire carbon fluxes in degraded systems and the potential to exacerbate or dampen the initial carbon losses. To better understand the impact of these disturbance interactions on the globally-important northern peatland carbon stock, we collated empirical datasets from natural, degraded and restored peatlands in non-permafrost regions to model net ecosystem exchange and methane fluxes, integrating peatland degradation status, wildfire combustion severity and post-fire dynamics. Here, I present the results of our study including the likely impacts of climate change over the remainder of the century.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".