Basal conditions of Denman Glacier from hydrology modeling and their application to various friction laws
Bibliographic record
Abstract
The key process of basal sliding in Antarctic glaciers is often incorporated into ice dynamics models via the use of a friction law, which relates the basal shear stress to the effective pressure. With few ice dynamics models actively coupled to subglacial hydrology models, the effects of subglacial hydrology often manifest in the friction coefficient – an unknown parameter in the friction law. We investigate the impact of friction coefficients for Denman Glacier, East Antarctica, by comparing Ice-sheet and Sea-level System Model (ISSM) inversion simulations using the effective pressure produced from the Glacier Drainage System (GlaDS) model compared with a typically prescribed effective pressure using a combination of ice overburden pressure and height above sea level (NO). We apply these comparative model runs for the Budd and Schoof friction laws. In regions of fast ice flow, we find a positive correlation between the GlaDS output effective pressure and the friction coefficient for the Schoof law. In addition, using the GlaDS output effective pressure compared to NO leads to a smoother friction coefficient as well as smaller differences between the simulated and observed surface velocity. In general we find that spatial variations in the Schoof law match more closely with the known physics of subglacial hydrology than the Budd law and therefore suggest that using the GlaDS output effective pressure compared to NO produces more realistic results. This demonstrates the need to couple ice sheet and subglacial hydrological systems to accurately represent ice flow.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".