Changes in lake area in response to thermokarst processes and climate in Old Crow Flats, Yukon
Bibliographic record
Abstract
Abstract Growing evidence indicates that lake‐dominated ecosystems at high latitudes are undergoing significant hydrological changes. Research examining these changes is complicated because both thermokarst and climatic processes likely influence lake dynamics. To examine the relative impacts of these processes in permafrost landscapes, we investigated the dynamics of lake area and number in Old Crow Flats (OCF), Yukon using historical air photos and satellite imagery. Between 1951 and 2007, OCF experienced a decline of ~6000 ha in total lake area but gained 232 lakes. Close to half (49%) of the difference in lake area was driven by the rapid and persistent drainage of 38 large lakes. These catastrophic drainages were associated with new or enlarged outlet channels, resulted in the formation of numerous residual ponds, and were likely driven by thermokarst processes. Our analysis shows that catastrophic lake drainages have become more than 5 times more frequent in recent decades. These changes are likely related to the impacts of increased temperature and precipitation on thermokarst processes. Fifty‐nine of the 170 intensively studied lakes showed either large bidirectional fluctuations or gradual cumulative declines. These changes affected a much smaller portion of OCF and were likely driven by interactions between increased precipitation and temperature and individual catchment characteristics. To anticipate landscape‐scale changes in these systems, and assess their impact on hydrology, wildlife habitat, and carbon storage, field research is required to better characterize the mechanisms responsible for changes.
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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 | 0.000 | 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".