Parametrization of peatland hydraulic properties for the Canadian land surface scheme
Bibliographic record
Abstract
A hydraulic parametrization is developed for peatland environments in the Canadian Land Surface Scheme (CLASS). Three ‐wetland soil classes account for the typical variation in the hydraulic characteristics of the uppermost 0.5 m of organic soils. Review of the literature reveals that saturated hydraulic conductivity varies from a median of 1.0 × 10−7m/s in deeply humified sapric peat to 2.8 × 10−4 m/s in relatively undecomposed fibric peat. Average pore volume fraction ranges from 0.83 to 0.93. Parameters have been designed for the soil moisture characteristic curves for fibric, hemic and sapric peat using the Campbell (1974) equation employed in CLASS, and the van Genuchten (1980) formulation. There is little difference in modelled soil moisture between the two formulations within the range of conditions normally found in peatlands. Validation of modelled water table depth and peat temperature is performed for a fen in northern Québec and a bog in north‐central Minnesota. The new parametrization results in a more realistic simulation of these variables in peatlands than the previous version of CLASS, in which unrealistic mineral soil “equivalents “ were used for wetland soil climate modelling.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".