Transient and Transition Factors in Modeling Permafrost Thaw and Groundwater Flow
Bibliographic record
Abstract
Permafrost covers approximately 24% of the Northern Hemisphere, and much of it is degrading, which causes infrastructure failures and ecosystem transitions. Understanding groundwater and heat flow processes in permafrost environments is challenging due to spatially and temporarily varying hydraulic connections between water above and below the near-surface discontinuous frozen zone. To characterize the transitional period of permafrost degradation, a three-dimensional model of a permafrost plateau that includes the supra-permafrost zone and surrounding wetlands was developed. The model is based on the Scotty Creek basin in the Northwest Territories, Canada. FEFLOW groundwater flow and heat transport modeling software is used in conjunction with the piFreeze plug-in, to account for phase changes between ice and water. The Simultaneous Heat and Water (SHAW) flow model is used to calculate ground temperatures and surface water balance, which are then used as FEFLOW boundary conditions. As simulating actual permafrost evolution would require hundreds of years of climate variations over an evolving landscape, whose geomorphic features are unknown, methodologies for developing permafrost initial conditions for transient simulations were investigated. It was found that a model initialized with a transient spin-up methodology, that includes an unfrozen layer between the permafrost table and ground surface, yields better results than with steady-state permafrost initial conditions. This study also demonstrates the critical role that variations in land surface and permafrost table microtopography, along with talik development, play in permafrost degradation. Modeling permafrost dynamics will allow for the testing of remedial measures to stabilize permafrost in high value infrastructure environments.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".