Towards quantifying the negative feedback regulation of peatland evaporation to drought
Bibliographic record
Abstract
Abstract The frequency and intensity of drought is projected to increase within the boreal region under future climatic conditions. Peatlands are widely considered to regulate water loss under drought conditions, increasing surface resistance ( r s ) and reducing evaporative losses. This maintains peat moisture content, increasing the resilience of these globally important carbon stores. However, the magnitude and form of this important negative feedback response remains uncertain. To address this, we monitored the response of r s to drought within four peat cores under controlled meteorological conditions. When the water‐table was dropped to a depth of 0.30 m and the humidity reduced to ≤40%, a step shift in r s from ~50 s m ‐1 up to 1000 s m ‐1 was observed within burned and unburned peat, which virtually shuts down evaporation, limiting water loss. We show that measured near‐surface tension cannot be used to directly calculate this transition in peat surface resistance. However, empirical relationships that account for strong vertical variations in tension through the near‐surface and/or disequilibrium between pore air and near‐surface pore water pressure provide the potential to incorporate this negative feedback response into peatland ecohydrological models. Further observations are necessary to examine this response under dynamic atmospheric conditions. We suggest that the link between surface temperature and evaporation provides potential to further examine this feedback in either burned peatlands or peatlands with a low vascular vegetation cover. 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".