Impacts of ecological succession and climate warming on permafrost aggradation in drained lake basins of the Tuktoyaktuk Coastlands, Northwest Territories, Canada
Bibliographic record
Abstract
Abstract Rapidly increasing air temperatures will alter permafrost conditions across the Arctic, but variation in soils, vegetation, snow conditions, and their effects on ground thermal regime complicate prediction across spatial and temporal scales. Processes that result in the emergence of new surfaces (lake drainage, channel migration, isostatic uplift, etc.) provide an opportunity to assess the factors influencing permafrost aggradation and terrain evolution under a warming climate. In this study we describe ground temperatures, vegetation, and snow and soil conditions at six drained lake basins (DLBs) that have exposed new terrain in the Tuktoyaktuk Coastlands in the last 20–100 years. We also use one‐dimensional thermal modeling to assess the impact of ecological succession and future climate scenarios on permafrost conditions in historical and future DLBs. Our field observations show that deep snow pack and shallow organic layers at shrub‐dominated DLBs promote increased thaw depth and ground temperatures compared to a sedge‐dominated DLB and two ancient DLB reference sites. Modeling of past and future drainages shows that climate warming projected under RCP 8.5 will reduce rates of permafrost aggradation and thickness, and drive top‐down thaw that could degrade permafrost in shrub‐dominated DLBs by the end of the century. Permafrost at sedge‐dominated sites was more resilient to warming under RCP 8.5, with the onset of top‐down thaw delayed until about 2080. Together, this indicates that the effects of ecological succession on organic soil development and snow drifting will strongly influence the aggradation and resilience of permafrost in DLBs. Our analysis suggests that DLBs and other emergent landscapes will be the first permafrost‐free environments to develop under a warming climate in the continuous permafrost zone.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".