Effect of Soil Thermal Heterogeneity on Permafrost Evolution
Bibliographic record
Abstract
The hydrology and carbon balance of cold regions are drastically influenced by the presence of permafrost in the subsurface. There is little work in the literature attending to the impact of heterogeneities upon the long-term and short-term evolution of permafrost bodies. This local heterogeneity, however, may be a major driver in talik formation which, in turn, can impact landscape evolution, hydrologic connectivity, and greenhouse gas emission. In this research, a freeze-thaw code based on the non-isothermal phase transition criterion (enthalpy-based) is used for studying the effect of heterogeneity on the soil-water-ice system’s thermal processes. A trust region algorithm is implemented for solving the highly non-linear system of equations. The results of the developed tool are successfully verified against the existing analytical solution for a three-zone one-dimensional medium presented by Lunardini (1985). Using the FEM-based tool developed in this research, two-dimensional freeze/thaw simulations are run in ensembles of spatially correlated heterogeneous soils with spatially heterogeneous freezing points in order to better elucidate the relative impact of various forms of heterogeneity on local permafrost table evolution. The results of the simulations of a domain with stochastically distributed thermal properties illustrate that local heterogeneity has conditional influence on the long-term permafrost body evolution and talik formations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".