The abundance of plunging flows in bedrock rivers
Bibliographic record
Abstract
Bedrock rivers are the keystone to understanding landscape evolution as they control the rate of geomorphic responses to climatic and tectonic signals. Bedrock erosion is driven by channel hydraulics, which are not well understood for complex bedrock river morphologies. Some bedrock rivers exhibit a constriction-pool-widening morphology and associated plunging flow where the core of maximum velocity follows the bed into deep pools. Previous observations suggest that moderate discharges enhance the erosive potential of plunging flows, and that plunging flows are the dominant driver of morphology and downcutting in reaches where they are present. However, there are very few observations of plunging flows in natural environments, so their frequency and cumulative impact on landscape evolution is still unclear. Here we examine Acoustic Doppler Current Profiler and Multibeam Echosounder data collected in the Fraser Canyon of British Columbia, Canada to better understand the general properties and frequency of plunging flows. The entire 375 kms of the Fraser Canyon were analyzed for indicators of plunging flows to estimate their frequency. Results suggest that plunging flows appear to be abundant and are correlated with high shear stresses which are concentrated into deep bedrock pools. When examined using common erosion modelling techniques, observations suggest that reaches with plunging flows are incising at a much faster rate than non-plunging reaches. This work shows that considering reach scale hydraulics is critical for understanding the morphogenesis of large bedrock rivers and the mountainous landscapes that they drain. The abundance of plunging flows in large bedrock rivers suggests the importance of integrating complex flow patterns into bedrock erosion models, informs patterns of bedrock erosion at the reach scale, and begs for further investigation into the distribution of fluid shear stresses in complex bedrock channel morphologies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".