Reduced basal motion responsible for 50 years of declining ice velocities on Athabasca Glacier
Bibliographic record
Abstract
Abstract The time-evolution of glacier basal motion remains poorly constrained, despite its importance in understanding the response of glaciers to climate warming. Athabasca Glacier provides an ideal site for observing changes in basal motion over long timescales. Studies from the 1960s provide an in situ baseline dataset constraining ice deformation and basal motion. We use two complementary numerical flow models to investigate changes along a well-studied transverse profile and throughout a larger study area. A cross-sectional flow model allows us to calculate transverse englacial velocity fields to simulate modern and historical conditions. We subsequently use a 3-D numerical ice flow model, Icepack, to estimate changes in basal friction by inverting known surface velocities. Our results reproduce observed velocities well using standard values for flow parameters. They show that basal motion declined significantly (30–40%) and this constitutes the majority (50–80%) of the observed decrease in surface velocities. At the same time, basal resistive stress has remained nearly constant and now balances a much larger fraction of the driving stress. The decline in basal motion over multiple decades of climate warming could serve as a stabilizing feedback mechanism, slowing ice transport to lower elevations, and therefore moderating future mass loss rates.
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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 | 0.000 |
| 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) | 0.001 | 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".