Numerical simulation of the impacts of climate warming on a permafrost mound
Bibliographic record
Abstract
Abstract A finite‐element, one‐dimensional, heat conduction model, which takes thaw settlement into account following drainage of excess water produced by the melt of ice lenses at the permafrost boundaries, is used to assess the thermal response of a permafrost mound in Northern Québec to different scenarios of climate warming. In addition to the cryostratigraphy of the mound, the unfrozen water content, thermal conductivity and volumetric heat capacity of the marine sediments comprising the mound were integrated in the simulation. Warming rates from 0.03 to 0.01°C/year are predicted in the mound for a gradual linear increase in mean annual air temperature of 0.05°C/year over a 100 year period. Downward thawing occurs at the permafrost table at rates of 1–13 cm/year but there is also upward thawing from the permafrost base at rates of 2.4–5.8 cm/year. The thermal response of permafrost is not linear with time. At the end of the 100 year period, thaw settlement predicted was 1.4 m and the active layer was 3.22 m thick over a talik of 1.46 m thick. This is in comparison to an active layer of 2.14 m thick over a talik of 1.86 m if thaw settlement is not considered in the simulation. Thaw settlement, the direct result of ice melting in permafrost, brings permafrost nearer to the surface and accelerates its thawing. It should be included in any numerical simulation. Copyright © 2004 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".