The effect of valley confluence and bedrock geology upon the location and depth of glacial overdeepenings
Bibliographic record
Abstract
Overdeepenings are erosional landforms, cut by glaciers into bedrock in basins and valleys. Overdeepening is the glaciological and geomorphological process that produces these landforms. The overdeepening process is important because it has the potential to influence the response of ice masses to climatic changes. In this paper, we analyze topographic and bathymetric digital elevation models to examine several hundred glacial overdeepenings in Labrador, Canada. We investigate controls upon the location and depth of overdeepenings. Our analyses show that the location of overdeepenings correlate strongly with confluences of glacial valleys and, importantly, that the correlation is strongest where confluence geometry requires the speed-up of ice-flow due to change in valley cross-sectional area. Further, we find that the magnitude of ice-flow speed-up correlates with depth of overdeepenings only for confluences situated in or near major geological fault-zones. Our findings therefore support the hypothesis that overdeepening can be initiated by an increase in ice velocity. Further, we conclude that overdeepening is most efficacious where fractured bedrock enables efficient quarrying. In summary, we find that the primary control upon the location of overdeepenings arises from confluences of glacial valleys due to ice speeding up at these locations, and that the depth of overdeepenings are controlled by rock mass strength. These findings are relevant for landscape evolution modelling and may be used in model testing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".