Numerical simulations of shallow groundwater flow and heat transport in continuous permafrost setting under impact of climate warming
Bibliographic record
Abstract
Numerical simulations of coupled groundwater flow and heat transport are used to address how hydrogeological conditions can affect permafrost dynamics. The simulations are based on a two-dimensional vertical-plane conceptual model of a study site at the Iqaluit Airport, Nunavut, Canada, which includes a 50 m deep permafrost terrain with a shallow active layer, overlain by a paved taxiway with winter snow-covered embankments. Coupled groundwater flow and advective–conductive heat transport with freeze–thaw dynamics, temperature-dependent pore-water freezing functions, and latent heat are included in the model. The simulation results show that a smooth (low-slope) freezing function with a higher residual unfrozen moisture content produced a deeper thaw front compared to that using a steeper freezing function, generating a maximum increase in the depth to permafrost of 17.5 m after 268 years. Permafrost thaw rates in high-permeability zones within a heterogeneous system were also relatively higher compared to an otherwise equivalent homogeneous soil, resulting in a maximum increase of 2.6 m in the depth to permafrost after 238 years. As recharge water cools while flowing along the upgradient permafrost table, advectively driven heat transport is paradoxically shown to temporarily increase the height of the permafrost table in downgradient areas.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".