Moss and peat hydraulic properties are optimized to maximize peatland water use efficiency
Bibliographic record
Abstract
Abstract Peatland ecosystems are globally important carbon and terrestrial surface water stores that have formed over millennia. These ecosystems have likely optimized their ecohydrological function over the long‐term development of their soil hydraulic properties. The optimization of peat hydraulic properties is examined to determine which of the following conditions peatland ecosystems target during this development: (i) maximize carbon accumulation, (ii) maximize water storage, or (iii) balance carbon profit across hydrological disturbances. To identify this control, the short‐term hydrological response of a 0.5‐m‐deep peat profile was simulated during a 50‐day rain‐free period. A total of 5000 Monte‐Carlo model realizations were conducted, with peat hydraulic properties differing between each realization (values derived from known probability distributions). Saturated hydraulic conductivity (Ks) and empirical van Genuchten water retention parameter α were shown to provide a first order control on simulated water tensions. For hypothetical combinations of Ks and α, the probability that water tension exceeds the ecologically important threshold of 100 mb within 24 h showed a bimodal distribution. A peak at high probabilities was associated with profiles of high Ks and low α. Such a profile is optimized for water storage. A peak at low probabilities is associated with low Ks, high α, and is optimized for carbon accumulation. Actual hydraulic properties from five northern peatlands fall between this binominal distribution, balancing the competing demands of carbon accumulation and water storage. We argue that peat hydraulic properties are thus optimized to maximize water use efficiency and that this optimization occurs over a centennial to millennial timescale. Copyright © 2015 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".