Retreat and frontal ablation rates for Alaska’s lake-terminating glaciers: Investigating potential physical controls with implications for future stability
Bibliographic record
Abstract
Abstract Globally, glaciers are changing in response to climate warming, with those that terminate in water often undergoing the most rapid change. In Alaska and northwest Canada, proglacial lakes have grown in number and size but their influence on glacier mass loss is unclear. We characterized the rates of retreat and mass loss through frontal ablation of 55 lake-terminating glaciers (>14 000 km2) in the region using annual Landsat imagery from 1984 to 2021. We find a median retreat rate of 60 m a−1 (interquartile range = 35–89 m a−1) over 1984–2018 and a median loss of 0.04 Gt a−1 (0.01–0.15 Gt a−1) mass through frontal ablation over 2009–18. Summed over 2009–18, our study glaciers lost 6.1 Gt a−1 to frontal ablation. Analysis of bed profiles suggest that glaciers terminating in larger lakes and deeper water lose more mass to frontal ablation, and that the glaciers will remain lake-terminating for an average of 74 years (38–177 a). This work suggests that as more proglacial lakes form and as lakes become larger, enhanced frontal ablation could cause higher mass losses, which should be considered when projecting the future of lake-terminating glaciers.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".