Groundwater connectivity controls peat burn severity in the boreal plains
Bibliographic record
Abstract
Abstract Wildfire is the largest disturbance affecting peatland ecosystems and can typically result in the combustion of 2–3 kg C m−2 of near‐surface peat. We hypothesized that organic soil burn severity, as well as the associated carbon emissions, varies significantly as a function of hydrogeological setting due to groundwater impacts on peat bulk density and moisture content. We measured depth of burn (DOB) in three peatlands located along a hydrogeological and topographic gradient in Alberta's Boreal Plains. Peatland margins across all hydrogeological settings burned significantly deeper (0.245 ± 0.018 m) than peatland middles (0.057 ± 0.002 m). Further, hydrogeological setting strongly impacted DOB. A bog with an ephemeral groundwater connection in a coarse‐textured glaciofluvial outwash experienced the greatest DOB at its margins (0.514 ± 0.018 m) due to large water table fluctuations, while a low‐lying oligotrophic groundwater flow‐through bog in a coarse‐textured glaciofluvial outwash experienced limited water table fluctuations and had the lowest margin burn severity (0.072 ± 0.002 m). In an expansive peatland in a lacustrine clay plain, DOB at the margins bordering an isolated domed bog portion (0.186 ± 0.003 m, range: 0.0–0.748 m) was considerably greater than the DOB observed at fen margins with a longer groundwater flow path (<0.05 m). Our research indicates that groundwater connectivity can have a dominant control on soil carbon combustion across and within hydrogeological settings. We suggest that hydrogeological setting be used to identify potential deep burning ‘hotspots’ on the landscape to increase the efficacy of wildfire management and mitigation strategies. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 | 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".