Evolution of subglacial overdeepenings in response to sediment redistribution and glaciohydraulic supercooling
Bibliographic record
Abstract
Abstract Glaciers erode bedrock rapidly, but evacuation of sediments requires efficient subglacial drainage networks. If glaciers erode more rapidly than evacuation proceeds, a protective subglacial till layer can form to armor the bed. Where glaciers cross overdeepenings, local closed depressions, the bed slope opposes the ice surface and lowers the hydraulic potential gradient that drives water flow. Here, we present results of a dynamic, distributed model of coupled basal water flow and sediment transport to show how overdeepenings evolve over the course of a melt season. We use steady‐state calculations as well as numerical simulations to understand how alluvial bed erosion alters overdeepenings. Numerical results from a modified form of the Spring‐Hutter equations show behaviors that cannot be inferred from either local or steady‐state calculations. In general, opposition of surface and bed slopes lessens sediment transport regardless of ice accretion from glaciohydraulic supercooling. Drainage efficiency strongly affects erosion and deposition rates. Results show characteristic behaviors of flow through overdeepenings such as overpressured water systems and accretion rates compatible with field measurements. Simulations that start with overdeepened glacier configurations progress out of a freezing regime where glaciohydraulic supercooling occurs. This progression indicates that glacier hydrology is more strongly affected by erosion and deposition than by freezing from glaciohydraulic supercooling. We discuss how this outcome affects glacier erosion and sediment transport under modern and past 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".