Effect of peat burn severity on peatland DOC concentration and DOM composition exported following wildfire
Bibliographic record
Abstract
Climate change is increasing boreal biome drying, area-burned, wildfire intensity, and burn severity as evidenced by the unprecedented 2023 Canadian wildfire season (>15 Mha burned). Of particular concern in boreal wildfires are deep burning smouldering peat fires that can switch peatlands to net emitters of atmospheric carbon. Less studied are the effects of peat fires on water-borne carbon and the deleterious impacts on downstream water quality as the burned area recovers post-fire. To better understand the impacts of wildfires on northern peatlands, we investigated the effects of varying peat burn severities on the dissolved organic carbon (DOC) concentration and composition of dissolved organic matter (DOM) exported from peatlands located in Ontario's Boreal Shield ecozone. Using a paired peatlands approach with twelve peatlands of comparable size and catchment, runoff and water quality were measured within the footprint of the Parry Sound #33 wildfire (burned) and near Dinner Lake (unburned). Over three years (2021-2023), exported DOC concentrations decreased with increasing burn severity but the composition of DOM varied across burn severities. Spectral slope (SR), SUVA254, and humification index (HIX) were utilized to assess DOM composition. Lower HIX and higher SR values were observed indicating smaller, less humified DOM as burn severity increased. SUVA254, however, showed no strong trends across burn severities suggesting that returning vegetation composition may have a strong control on DOM composition. Considering that climate change is increasing burn severity, the recovery of burned peatlands may play a large role in the export of DOC concentration and DOM composition post-wildfire.
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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".