Numerical modelling of permafrost dynamics under climate change and evolving ground surface conditions: application to an instrumented permafrost mound at Umiujaq, Nunavik (Québec), Canada
Bibliographic record
Abstract
Numerical simulations were carried out based on a conceptual cryohydrogeological model of a permafrost mound near Umiujaq, Nunavik (Québec), Canada, to assess the impacts of climate warming and changes in surface conditions on permafrost degradation. The 2D model includes groundwater flow, advective-conductive heat transport, phase change and latent heat. Changes in surface conditions which are characteristic of the site were represented empirically in the model by applying spatially- and temporally-variable ground surface temperatures derived from linear regressions between monitored surface and air temperatures. After reaching a transient steady-state condition close to present-day conditions, the simulations were then extended to 2100 under hypothetical climate warming scenarios and using imposed changes in surface conditions consistent with observed on-site evolution. The simulations show that the development of a thermokarst pond and shrubification respectively induce ground warming of up to 0.5°C and 1.5°C, upward migration of the permafrost base by up to 2 and 4 m, and a decrease in the lateral permafrost extent of 1 and 7 m, relative to a reference case without changes in surface conditions. Feedback from permafrost degradation which drives changes in ground surface conditions should be included in future numerical modelling of permafrost dynamics.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".