Postglacial topographic evolution of glaciated valleys: a stochastic landscape evolution model
Bibliographic record
Abstract
Abstract The retreat of valley glaciers has a dramatic effect on the stability of glaciated valleys and exerts a prolonged influence on the subsequent fluvial sediment transport regime. We have studied the evolution of an idealized glaciated valley during the period following retreat of ice using a numerical model. The model incorporates a stochastic process to represent deep‐seated landsliding, non‐linear diffusion to represent shallow landsliding and an approximation of the Bagnold relation to represent fluvial sediment transport. It was calibrated using field data from several recent surveys within British Columbia, Canada. We present ensemble model results and compare them with results from a deterministic linear‐diffusion model to show that explicit representation of large landslides is necessary to reproduce the morphology and channel network structure of a typical postglacial valley. Our model predicts a rapid rate of fluvial sediment transport following deglaciation with a subsequent gradual decline, similar to that inferred for Holocene time. We also describe how changes in the model parameters affect the estimated magnitude and duration of the paraglacial sediment pulse. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".