A model for polythermal ice incorporating gravity-driven moisture transport
Bibliographic record
Abstract
The flow of ice sheets and glaciers dissipates significant amounts of heat, which can result in the formation of ‘temperate ice’, a binary mixture of ice and small amounts of melt water that exists at the melting point. Many ice masses are polythermal, in the sense that they contain cold ice, below the melting point, as well as temperate ice. Temperature and melt water (or moisture) content conversely affect the flow of these ice masses through their effect on ice viscosity and sliding behaviour. Ice flow models therefore require a component that can solve for temperature and moisture content, and determine the free boundary between the cold and temperate subdomains. We present such a model, based on the theory of compacting partial melts. By contrast with other models, we describe gravity- and pressure-gradient-driven drainage of moisture, while maintaining a divergence-free ice flow at leading order. We also derive the relevant boundary conditions at the free cold–temperate boundary, and find that the boundary behaves differently depending on whether ice enters or exits the temperate region. The paper also describes a number of test cases used to compare with a numerical solution, and investigates asymptotic solutions applicable to the limit of small compaction pressure gradients in the temperate ice regions. A simplified enthalpy-gradient model is finally proposed, which captures most of the behaviour of the full model in this limit.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".