Century-Scale Shifts in Peat Hydro-Physical Properties as Induced by Drainage
Bibliographic record
Abstract
Hydro-physical properties of peat influence the partitioning of rainfall into infiltration versus runoff, determine water flow and solute transport patterns, and regulate the carbon and nitrogen cycles in peatlands. Although soil hydro-physical properties of peat soils are well documented, little is known about the temporal dynamics of soil properties, especially at a century-scale. A data set of peat subsidence as well as bulk density (BD) increase rate following artificial drainage was assembled from the literature. The collected data cover a time period of up to 215 years of land drainage for different land use types (forest and agriculture). The results show that the subsidence rate and soil BD increase rate generally depend on land drainage duration and land use. The most severe shift in soil pore structure of peat used for forest and agricultural occurs within the first 10 and 40 years of land drainage, respectively. Peatland drainage reduces the number of macropores (>50 μm) but increases ultramicro- and cryptoporosity (<5 μm). In the long term, peat subsidence is responsible for more than 85% of soil water storage loss. In conclusion, the derived functions between subsidence rate as well as BD increase rate and drainage duration provide a new method to estimate hydro-physical properties (pore structure and saturated hydraulic conductivity, specific yield, soil water storage) of peat at a century-scale. The derived hydro-physical parameter values can be used for long-term hydrological modelling (back- and forward), especially if measured hydraulic parameters of peat are not available. However, additional research is required to reduce uncertainty.
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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 | 0.001 | 0.001 |
| 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".