Draining and filling of ice-dammed lakes at the terminus of surge-type Dań Zhùr (Donjek) Glacier, Yukon, Canada
Bibliographic record
Abstract
Recent surges of Dań Zhùr (Donjek) Glacier have formed lakes at the glacier terminus that have drained catastrophically, resulting in hazards to people and infrastructure downstream. Here we use air photos and satellite imagery to describe lake formation, and the timing of filling and draining, since the 1930s. Between the 1930s and late 1980s, lakes were typically small (<0.6 km2), took many years to form after a surge event, and drained slowly as they were displaced by the glacier advancing in the next surge. However, since 1993, the lakes have become larger (>1 km2) and drain rapidly through or under the glacier by breaking a terminal ice dam. For the past two surges, since 2001, the lakes formed during or immediately after a surge in an increasingly larger basin between the Neoglacial maximum moraine and an increasingly smaller maximum terminus extent. Most recently, the 2012–2014 surge created a lake that drained in summer 2017, refilled, and drained again in both summer 2018 and summer 2019. The 2019 lake was 2.2 km2, the largest on record, and drained entirely within 2 days. While a lake is unlikely to form again before the next expected surge in the mid-2020s, future surges of Dań Zhùr Glacier are still likely to create terminal lakes, necessitating continued monitoring for surge activity and lake formation.
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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 | 0.002 | 0.001 |
| 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".