Degradation of ice-wedge polygons leads to increased fluxes of water and DOC
Bibliographic record
Abstract
Ice-wedge polygon landscapes make up a substantial part of high-latitude permafrost landscapes. The hydrological conditions shape how these landscapes store and release organic carbon. However, their coupled water‑carbon dynamics are poorly understood as field measurements are sparse in smaller catchments and coupled hydrology-dissolved organic carbon (DOC) models are not tailored for these landscapes. Here we present a model that simulates the hydrology and associated DOC export of high-centered and low-centered ice-wedge polygons and apply the model to a small catchment with abundant polygon coverage along the Yukon Coast, Canada. The modeled seasonal pattern of water and carbon fluxes aligns with sparse field data. These modeled seasonal patterns indicate that early-season runoff is mostly surficial and generated by low-centered polygons and snow trapped in troughs of high-centered polygons. High-centered polygons show potential for deeper subsurface flow under future climate conditions. This suggests that high-centered polygons will be responsible for an increasing proportion of annual DOC export compared to low-centered polygons. Warming likely shifts low-centered polygons to high-centered polygons, and our model shows that this shift will cause a deepening of the active layer and a lengthening of the thawing season. This, in turn, intensifies seasonal runoff and DOC flux, mainly through its duration. Our model provides a physical hypothesis that can be used to further quantify and refine our understanding of hydrology and DOC export of arctic ice-wedge polygon terrain.
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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 | 0.000 |
| 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".