Quantifying peat hydrodynamic properties and their influence on water table depths in peatlands of southern Quebec (Canada)
Bibliographic record
Abstract
Abstract Water table depth in peatlands is strongly linked to physical properties of the peat, such as density (ρdry), peat composition and humification, hydraulic conductivity (K), and specific yield (Sy). Dry bulk density and peat depth are commonly used as indicators of K in ecohydrological models. However, no mathematical relationship exists to quantify Sy based on K and ρdry. As a result, ecohydrological models cannot explicitly reproduce the strong buffering capacity of peatlands. The objectives of this study were to analyse the literature‐reported mathematical link between all the physical properties to develop new mathematical relationships between these parameters and to evaluate whether variations in the physical properties of the peat control water table depth in peatlands. Seven peatlands located in the St. Lawrence Lowlands (Québec, Canada) were sampled, and 1 m long peat cores were collected from up‐gradient, mid‐gradient, and down‐gradient zones. All cores were used to measure ρdry, K, Sy, and to estimate peat composition and humification. Statistically significant correlations were found between (a) K and Sy (log–log model), (b) K and depth (log–log model), (c) Sy and depth (log–log model), (d) ρdry and Sy (log model), and (e) ρdry and K (log model). No significant difference was found in either K or Sy between sites. However, significant differences were found in water table depths. Because they provide a fuller description of the peat properties that control water table depths, these newly developed functions have the potential to improve the capacity of ecohydrological models to simulate time‐varying hydrological conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".