A Framework for Understanding the Impacts of Thaw‐Driven Disturbance Regimes on Northern Lakes
Bibliographic record
Abstract
ABSTRACT The impacts of permafrost thaw do not manifest uniformly across the Arctic, and this presents challenges for predicting how permafrost‐affected lakes will respond to climate change. Here, we leveraged long‐term field data collection and studies of a particular permafrost lake type (predisposed to thaw slump disturbance) in the Tuktoyaktuk Coastlands region to develop a conceptual model that can shed light on, and facilitate further testing of, lake ecosystem impacts associated with polycyclic shoreline retrogressive thaw slumps. To inform the development of the model, we compared regional changes in slump activity and lake water quality variables known to be impacted by slumping between 2005 and 2017, and showed that the timeline to recovery in lake dissolved organic carbon (DOC) following slump stabilization was longer than the time between cycles of slump growth, stabilization, and re‐initiation in most lakes. We also analyzed geochemical changes in sediment cores that incorporated the last several hundred years of history in 18 lakes that spanned a gradient in thaw slump disturbance, as well as a core dating back to ca. 1365 CE in a lake with an ancient slump. Results indicate that sediment geochemical changes were most pronounced at the initial onset of slumping, with only muted changes in sediment characteristics upon slump growth or re‐initiation. Our conceptual model can guide future investigations into the extent to which lakes with polycyclic slumps are predisposed to, or buffered against, significant limnological changes under anthropogenic climate warming. While our model is specific to lake systems in ice‐cored morainal terrain impacted by retrogressive thaw slumping, similar approaches would be highly useful for understanding ecosystem response under a range of permafrost disturbance categories.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".