Significant subglacial and proglacial lake drainages in the Canadian Arctic identified by time-stamped ArcticDEM strips
Bibliographic record
Abstract
Active subglacial and proglacial lakes drain and fill constantly, contributing to glacier and ice sheet mass balance in a way that is not always easy to quantify. Active subglacial lakes account for about 20% of the first worldwide subglacial lake inventory (published by Livingstone et al., 2022); however, none have been previously identified in the Canadian Arctic. Here, we report at least 28 drainage and fill events identified by analyzing time-stamped ArcticDEM elevation data. We stack all the available 2-m DEM strips (23,691 in total) from the latest ArcticDEM release (October 2022) and calculate the elevation change rate at every 15-m sized pixel in a reference grid. Glacier areas with the following signals are interpreted to be associated with the lake drainage or refill beneath the ice: (1) a significantly higher elevation change rate than the neighboring regions within the same glacier catchment, and (2) no adjacent zones showing reversed elevation change (to avoid surge event being misclassified). If such an area touches the glacier terminus, we interpret the elevation change to be governed by a proglacial lake where the floating ice terminus rises and falls when the lake level changes. These drainage and refill events are scattered throughout the region, from the North Ellesmere Icefields to Penny Ice Cap (South Baffin Island). Almost none of the lake locations have been previously reported, probably due to their small size (a few kilometers wide on average), but some of them caused significant ice elevation drops of up to 100 meters during a drainage event. It is not clear whether these significant drainage events produced outburst floods due to temporal sampling gaps in the data. Nevertheless, the water mass lost or gained during the events should be independently calculated from the land ice budget, and we should keep monitoring these newly discovered lakes for their potential impact on the ice flow dynamics and localized mass balances, especially in the context of rapid Arctic warming.
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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 | 0.007 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".