Effect of drainage and wildfire on peat hydrophysical properties
Bibliographic record
Abstract
Abstract Consecutive multiple disturbances to northern peatlands can dramatically impact peat hydrophysical properties. We examine the impact of a double disturbance (drainage and wildfire) on the hydrophysical and moisture retention properties of peat, a key regulator of peatland ecohydrological resilience, and compare this with the impact to each individual disturbance (drainage and wildfire). The compound effect of drainage and wildfire resulted in a shift of the surface datum down the peat profile, revealing denser peat. Less‐dense near‐surface peats that regulate water‐table position and near‐surface moisture content, both favourable to Sphagnum recolonization, were lost. At a drained peatland that was then subject to wildfire, peat bulk density increased by 14.1%, von Post humification class increased by two categories and water retention increased by 15.6%, compared with an adjacent burned but undrained (single disturbance) portion of the fen. We discuss the key hydrophysical metrics of peatland vulnerability and outline how they are affected by the isolated impacts of drainage and wildfire, as well as their combined effects. We demonstrate that multiple peatland disturbances have likely led to an increase in hydrological limitations to Sphagnum recovery, which may impact peatland ecohydrological resilience. Copyright © 2013 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 | 0.000 |
| 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".