Dynamics and mass balance of Taylor Glacier, Antarctica: 2. Force balance and longitudinal coupling
Bibliographic record
Abstract
Taylor Glacier, Antarctica, exemplifies an ice sheet outlet that flows through a region of rugged topography and dry climate. In contrast to other well‐studied outlets, Taylor Glacier moves very slowly, despite a thickness of order 1 km and driving stresses averaging 1.5 bars. Here we analyze new measurements of glacier geometry and surface velocity to elucidate flow dynamics of Taylor Glacier. Force balance and basal temperatures are calculated at six locations along the glacier's length using an algorithm developed for this study. The effects of stress‐gradient coupling on longitudinal flow variations are also examined; we ask whether Kamb and Echelmeyer's (1986) linearized theory adequately describes the observed response of flow to large‐amplitude variations in driving stress. The force balance calculations indicate that no basal motion is needed to explain the observed flow of Taylor Glacier. Inferred basal temperatures are within a few degrees of the melting point in regions of kilometer‐thick ice and well below the melting point elsewhere; deformation of subfreezing ice largely controls the flow of Taylor Glacier. Basal drags are mostly in the range 0.9 to 1.2 bars, and lateral drags are in the range 0.2 to 0.5 bar. Stress‐gradient coupling strongly reduces the variability of velocities along the glacier. The velocity variations can be described as the convolution of a forcing function with a spatial filter, as Kamb and Echelmeyer suggested, but the form of the forcing function differs from the theoretical relation derived for small‐amplitude perturbations (the power on driving stress is one, not three).
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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".