Exploiting high-slip flow regimes to improve inference of glacier bed topography
Bibliographic record
Abstract
Abstract Theory and observation show that glacier-flow regimes characterized by high basal slip enhance the projection of topographic detail to the surface, motivating this investigation into the efficacy of using glacier surges to improve bed estimation. Here we adapt a Bayesian inversion scheme and apply it to real and synthetic data as a proof of concept. Synthetic tests show a reduction in mean RMSE between true and inferred beds by more than half, and an increase in the mean correlation coefficient of ~0.5, when data from slip- versus deformation-dominated regimes are used. Multi-epoch inversions, which partition slip- and deformation-dominated regimes, are shown to outperform inversions that average over these flow regimes thereby squandering information. Tests with real data from a surging glacier in Yukon, Canada, corroborate these results, while highlighting the challenges of limited or inconsistent data. With the growing torrent of satellite-based observations, fast-flow events such as glacier surges offer potential to improve bed estimation for some of the world's most dynamic 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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".