Glacial lakes inventory and susceptibility assessment in the Alsek River Basin, Yukon, Canada
Bibliographic record
Abstract
Abstract Background This study investigates glacial lake outburst floods (GLOFs) within the Alsek River Basin, Yukon, Canada, a region experiencing accelerated glacier retreat due to climate change. The formation and expansion of glacial lakes pose significant hazards to geomorphological and ecological systems, even in the absence of human infrastructure. Despite extensive research in other glaciated regions such as the Himalayas and Andes, the Canadian Cordillera remains understudied. This research aims to inventory glacial lakes and assess their susceptibility to GLOFs using remote sensing techniques and two distinct methodologies. Results A total of 590 glacial lakes were identified, with 57 in direct or indirect contact with glaciers, warranting a detailed susceptibility assessment. The study applied the glacier-focused methodology of Wang et al. (Mt Res Dev 31(2):122 (2011). https://doi.org/10.1659/mrd-journal-d-10-00059.1 ) and the lakespecific dynamics approach of Khadka et al. (Front Earth Sci 8(January):1–16 (2021). https://doi.org/10.3389/feart.2020.601288 ). Key findings include: High-Hazard Lakes: Lakes 22, 23, 133, 134, and 275 were consistently identified as high-hazard due to factors such as large glacier inputs, steep moraine dams, and rapid expansion rates. GLOF Events: Four GLOF events were confirmed between 2017 and 2019, with the most significant reducing Lake 21's area by over 80%. Comparative Analysis: The integration of both methodologies provided a comprehensive understanding, revealing complementary insights into glacier-driven and lake-specific GLOF triggers. Conclusion The results underscore the critical role of glacier retreat and lake dynamics in driving GLOF hazards in the Alsek River Basin. The study highlights the importance of combining multiple assessment methodologies for robust hazard evaluation. Given the dynamic nature of glacial lakes and ongoing climate change, continuous monitoring and proactive hazard management strategies are essential to mitigate potential geomorphological and ecological impacts. This research contributes to the broader understanding of GLOFs in North America and underscores the need for similar assessments in other understudied glaciated regions.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".