Controls on boreal peat combustion and resulting emissions of carbon and mercury
Bibliographic record
Abstract
Warming in the boreal forest region has already led to changes in the fire regime. This may result in increasing fire frequency or severity in peatlands, which could cause these ecosystems to shift from a net sink of carbon (C) to a net source of C to the atmosphere. Similar to C cycling, peatlands serve as a net sink for mercury (Hg), which binds strongly to organic matter and accumulates in peat over time. This stored Hg is also susceptible to re-release to the atmosphere during peat fires. Here we investigate the physical properties that influence depth of burn in experimental peat columns and the resulting emissions of CO, CO 2 , CH 4 , and gaseous and particulate Hg. As expected, bulk density and soil moisture content were important controls on depth of burn, CO 2 emissions, and CO emissions. However, our results show that CH 4 and Hg emissions are insensitive to combustion temperature or fuel moisture content. Emissions during the burning of peat, across a wide range of moisture conditions, were associated with low particulate Hg and high gaseous Hg release. Due to strong correlations between total Hg and CO emissions and because high Hg emissions occurred despite incomplete combustion of total C, our results suggest that Hg release during peat burning is governed by the thermodynamics of Hg reduction more so than by the release of Hg associated with peat combustion. Our measured emissions ratios, particularly for CH 4 :CO 2 , are higher than values typically used in the upscaling of boreal forest or peatland fire emissions. These emission ratios have important implications not only for our understanding of smouldering chemistry, but also for potential influences of peat fires on the Earth's climate system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".