A model of unfrozen water content and its transport in icy permafrost soils: Effects on ground ice content and permafrost stability
Bibliographic record
Abstract
Abstract Knowledge of the amount of unfrozen water and its migration in permafrost soils is important for understanding physico‐chemical and biological processes. Here, we developed sub‐routines in FREZCHEM and embedded them in the WATEREGO2 soil environmental model to: (a) estimate unfrozen water content under changing soil temperatures and water–ice phase changes; and (b) determine the effects of Van der Waals (VdW) and rheological forces driven by seasonal temperature variations on the transport of residual water and the long‐term evolution of ground ice content over depths of 30 m. Together, the seasonal thermal regime and associated VdW and rheological forces on the transport of residual water lead to the evolution of distinct zones of ice‐enrichment: near the surface of permafrost, at 3–5 m, 11–13 m and 17–19 m depth. The depths of ice enrichment are a function of soil thermal diffusivity, and the time needed to evolve the ground ice content is dependent on soil type, soil water chemistry and permafrost temperature. The model can explain observed variations with depth in ground ice content of icy permafrost soils and indicate that these conditions evolve over time. The findings can be used to assess the stability of permafrost to climate change under different temperature scenarios.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".