Comment on egusphere-2023-2794
Bibliographic record
Abstract
Abstract. Ice-sheet models used to predict sea-level rise often neglect subglacial hydrology. However, theory and observations suggest that ice flow and subglacial water flow are bidirectionally coupled: ice flow and geometry affects hydraulic potential, hydraulic potential modulates basal shear stress via the basal water pressure, and ice flow advects the subglacial drainage system. This coupling could impact rates of ice mass change, but remains poorly understood. We combine a channelized subglacial hydrology model with a depth-integrated marine-ice-sheet model, incorporating each component of the coupling listed above, which yields a set of differential equations that we solve using a finite-difference, implicit time-stepping approach. We conduct a series of experiments with this model, using either bidirectional or unidirectional coupling. These experiments generate steady-state profiles of channel cross-sectional area, channel flow rate, channel effective pressure, ice thickness, and ice velocity. We discuss how the steady-state profiles shape one another, resulting in the effective pressure reaching a local maximum in a region near the grounding line. We also describe the impact of bidirectional coupling on the transient retreat of ice sheets through a comparison of our coupled model with ice-flow models that have imposed static basal conditions. We find that including coupled subglacial hydrology leads to grounding-line retreat that is virtually absent when static basal conditions are assumed. This work highlights the role time-evolving subglacial drainage may have in ice-sheet change and informs efforts to include it in ice-sheet models.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.471 | 0.343 |
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".