Efficacy of bedrock erosion by subglacial water flow
Bibliographic record
Abstract
Abstract. Bedrock erosion by sediment-bearing subglacial water remains little-studied, however the process is thought to contribute to bedrock erosion rates in glaciated landscapes and is implicated in the excavation of tunnel valleys and the incision of inner gorges. We adapt physics-based models of fluvial abrasion to the subglacial environment, assembling the first model designed to quantify bedrock erosion caused by transient subglacial water flow. The subglacial drainage model consists of a one-dimensional network of cavities dynamically coupled to one or several Röthlisberger channels (R-channels). The bedrock erosion model is based on the tools and cover effect, whereby particles entrained by the flow impact exposed bedrock. We explore the dependency of glacial meltwater erosion on the structure and magnitude of water input to the system, the ice geometry and the sediment supply. We find that erosion is not a function of water discharge alone, but also depends on channel size, water pressure and on sediment supply, as in fluvial systems. Modelled glacial meltwater erosion rates are one to two orders of magnitude lower than the expected rates of total glacial erosion required to produce the sediment supply rates we impose, suggesting that glacial meltwater erosion is negligible at the basin scale. Nevertheless, due to the extreme localization of glacial meltwater erosion (at the base of R-channels), this process can carve bedrock (Nye) channels. In fact, our simulations suggest that the incision of bedrock channels several centimetres deep and a few meters wide can occur in a single year. Modelled incision rates indicate that subglacial water flow can gradually carve a tunnel valley and enhance the relief or even initiate the carving of an inner gorge.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".