Experiments on the morphological controls of velocity inversions in bedrock canyons
Bibliographic record
Abstract
Abstract A better understanding of bedrock incision mechanisms and processes is essential to the study of long‐term landscape evolution. Yet, little is known about flow dynamics in bedrock rivers, limiting our ability to make realistic predictions of local bedrock incision rates. A recent investigation of flow through bedrock canyons of the Fraser River revealed that plunging flows, defined by the downward‐directed movement of near surface flow toward the channel bed, occur in channels that have low width‐to‐depth ratios. Plunging flows occur into deep scour pools, which are often coincident with lateral constrictions and channel spanning submerged ridges (sills). A phenomenological investigation was undertaken to reproduce the flow fields observed in the Fraser canyons and to explore morphological controls on the occurrence and relative strength of plunging flow in bedrock canyons. Our observations show that the plunging flow structure can be produced along a scour pool entrance slope by accelerating the flow at the canyon entrance either over submerged sills or through lateral constrictions. Plunging flow appears to be a function of convective deceleration into a scour pool which can be enhanced by sill height, the amount of the channel width that is constricted, pool entrance slope, discharge, and a reduction in channel width‐to‐depth ratio. Plunging flow greatly enhances the potential for incision to occur along the channel bed and is an extreme departure from the assumptions of steady, uniform flow in bedrock incision models, highlighting the need for improved formulations that account for fluid flow. Copyright © 2017 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".