Radar-derived ice fabric anisotropy and implications on flow enhancement along the Thwaites Glacier Eastern Shear Margin
Bibliographic record
Abstract
Glaciers and ice streams account for the majority of ice mass discharge to the ocean from the Antarctic Ice Sheet, and are bounded by intense bands of shear that separate fast-flowing from slow or stagnant ice, called shear margins. The anisotropy of glacier ice (i.e. a preferred crystal orientation) stemming from high rates of shear at these margins can greatly facilitate fast streaming ice flow, however it is still poorly understood due to a lack of in-situ measurements. If anisotropy is incorporated into numerical ice sheet models at all, it is usually as a simple scalar enhancement factor that represents the "flow law" that governs the model's rheology. Ground-based and airborne radar observations along two transects fully crossing the Eastern Shear Margin of Thwaites Glacier reveal rapid development of highly anisotropic fabric tightly concentrated around a lateral maximum in surface shear strain. These measurements of fabric strength at the centre of the shear margin are indicative of a horizontal pole configuration, which potentially represents ice that is “softened” to shearing in some directions and hardened in others. The resulting flow enhancement revealed by our results suggest that the viscosity of ice is highly variable and regime-dependent, and supports the importance of considering anisotropic flow laws to model the rheology of ice sheets.
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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 | 0.001 | 0.000 |
| 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".