Surface Water Dynamics and Rapid Lake Drainage in the Western Canadian Subarctic (1985–2020)
Bibliographic record
Abstract
Abstract The area and distribution of surface water are shifting rapidly in many regions across the circumpolar Arctic. In this study, we explore the effects of climate and terrain factors on the area of lakes in the Northwest Territories, Canada. We used the Landsat satellite archive to map interannual changes in 5,328 lakes and ponds in the Lower Mackenzie Plain between 1985 and 2020. The high temporal resolution of our dataset allowed us to classify gradual and abrupt changes in lake area and identify rapid drainage events. We used Generalized Additive Models and Random Forests to test the effects of climate and terrain factors on changes in lake area. Despite increases in the area of smaller lakes driven by increasing precipitation, we found that the total lake area has decreased by approximately 1%. Overall, 29% of lakes exhibited an increasing trend in the area, while 11% exhibited a decreasing trend, and the majority of these changes (65%) were non‐linear in nature. Lakes located in fire scars were also 3.8 times more likely to show a decreasing trend in area. Analysis of a large fire indicates that lakes within the burned region exhibited declines in an area that persisted until the end of the study period 20 years after the fire. These declines are likely related to the impact of fire on thaw depth, groundwater connectivity, and the development of new drainage pathways. Our results highlight the importance of rapid drainage and wildfire as drivers of declines in the lake area.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".