Subglacial lake activity beneath the ablation zone of the Greenland Ice Sheet
Bibliographic record
Abstract
Hydrologically active subglacial lakes can drain large volumes of water and sediment along subglacial pathways, affecting the motion and mass balance of ice masses and impacting downstream sediment dynamics. To date, only eight active lakes have been reported beneath the Greenland Ice Sheet (GrIS), and thus the understanding of their spatial distribution and dynamic processes is still lacking. Here, using ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) ATL11 data, we identify 18 active subglacial lakes, 16 of which have not been previously reported. Multi-temporal ArcticDEM (digital elevation model of the Arctic) strip maps were used to extend the time series to verify lakes and determine their drainage history. The identification of active subglacial lakes beneath the GrIS is complicated by the occurrence of supraglacial lakes, which also fill and drain and are hypothesized to be almost co-located. We therefore used the temporal pattern of ice-surface elevation change to discriminate subglacial lakes and utilized the ability of ICESat-2 to penetrate through surface water to correct the elevation provided by the ATL11 data. A significant localized elevation anomaly (−16.03–10.30 m yr −1 ) was measured in all detected subglacial lakes after correction, revealing that six subglacial lakes are twinned with supraglacial lakes. The active subglacial lakes have large upstream hydrological catchments and are located near or below the equilibrium line. Lakes have a median area of 1.20 km 2 , and 12 lakes exhibited positive elevation-change rates during the ICESat-2 period. These observations illustrate the potential for combining ICESat-2 and the ArcticDEM to differentiate small subglacial lakes in the ablation zone and beneath supraglacial lakes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".