Drainage subsidence associated with Arctic permafrost degradation
Bibliographic record
Abstract
Arctic sources of greenhouse gas associated with permafrost degradation constitute a large uncertainty in existing climate models. Greenhouse gas release from the Arctic subsurface is mediated by numerous interconnected physical processes; one facet of these is the interplay between surface deformation and melting of subsurface ice. First, we construct analytic solutions describing fluid drainage and soil subsidence subsequent to thawing of a 1‐D permafrost column. These solutions lead to formulas giving the total amount of subsidence as well as the time over which subsidence occurs. We give an example application of the analytic model to peat plateau degradation in the Canadian Hudson Bay Lowland and show that the degree of subsidence predicted from our model is consistent with recorded subsidence of peat in western Norway that was drained for cultivation purposes. Second, we numerically model an initially frozen, fluid‐saturated, 2‐D soil matrix with a thaw zone advancing from the surface downward. With the surface temperature fixed at 5°C, a thaw front propagates to ∼10 m depth within 20 years, and due primarily to drainage of fluid from the pore space, a region of soil depressed by ∼3 m forms above an initially ice‐rich subsurface zone. Soil underlying this depressed zone may have its permeability reduced by between 1 and 2 orders of magnitude; this reduction in permeability can act as a negative feedback to thawing.
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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 | 0.000 | 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".