Excavation of subglacial bedrock channels by seasonal meltwater flow
Bibliographic record
Abstract
Abstract Subglacial water flow drives the excavation of a variety of bedrock channels including tunnel valleys and inner gorges. Subglacial floods of various magnitudes – events occurring once per year or less frequently with discharges larger than a few hundred cubic metres per second – are often invoked to explain the erosive power of subglacial water flow. In this study we examine whether subglacial floods are necessary to carve bedrock channels, or if more frequent melt season events (e.g. daily production of meltwater) can explain the formation of substantial bedrock channels over a glacial cycle. We use a one‐dimensional numerical model of bedrock erosion by subglacial meltwater, where water flows through interacting distributed and channelized drainage systems. The shear stresses produced drive bedrock erosion by bed‐ and suspended‐load abrasion. We show that seasonal meltwater discharge can incise an incipient bedrock channel a few tens of centimetres deep and several metres wide, assuming abrasion is the only mechanism of erosion, a particle size of D =256 mm and a prescribed sediment supply per unit width. Using the same sediment characteristics, flood flows yield wider but significantly shallower bedrock channels than seasonal meltwater flows. Furthermore, the smaller the shear stresses produced by a flood, the deeper the bedrock channel. Shear stresses produced by seasonal meltwater are sufficient to readily transport boulders as bedload. Larger flows produce greater shear stresses and the sediment is carried in suspension, which produces fewer contacts with the bed and less erosion. We demonstrate that seasonal meltwater discharge can excavate bedrock volumes commensurate with channels several tens of metres to a few hundred metres wide and several tens of metres deep over several thousand years. Such simulated channels are commensurate with published observations of tunnel valleys and inner gorges. Copyright © 2018 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".