A simple heat‐conduction method for simulating the frost‐table depth in hydrological models
Bibliographic record
Abstract
Abstract Hillslope runoff in permafrost regions covered by organic soil is strongly influenced by subsurface flow in the active layer, as well as surface flow where the active layer is very shallow. Flow rates in the organic‐rich active layer are strongly dependent on the depth to the thawing front (i.e. frost table) and the corresponding soil hydraulic conductivity at that depth. Therefore, hydrological models for permafrost terrains need to simulate the thawing of the active layer accurately. In order to simulate the downward movement of the frost table, a simple heat‐conduction model was proposed and compared to field data from a wet, organic‐covered watershed in a discontinuous permafrost region of Canada. Ground heat flux was measured simultaneously using the calorimetric, gradient, and flux‐plate methods to increase the confidence in data sets. The majority (>86%) of ground heat flux was used to melt the ice in frozen soil, and the soil temperature had a linear profile from the ground surface to the frost table when averaged over several days. Assuming a linear temperature profile, the proposed method calculates the daily rate of thawing from ground surface temperature and bulk thermal conductivity, where the latter is essentially determined by soil water content. Simulated depths to the frost table during three thaw seasons (2003–2005) matched closely with the observed data for two contrasting ground‐cover types with distinctly different thaw rates. The method can be easily implemented in hydrological models, and will provide a useful tool for simulating hillslope drainage in organic‐covered permafrost terrains, and for evaluating the effects of topography and land cover on the temporal and seasonal variability of the frost table. Copyright © 2007 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".