Bed and Bank Stress Partitioning in Bedrock Rivers
Bibliographic record
Abstract
Abstract Approximation of bed and wall (bank) stresses in confined, narrow bedrock rivers is key to accurately assessing hydraulic roughness, sediment transport, bedrock erosion and the morphodynamics of bedrock channels. Here, we partition bed and wall stresses using the ray‐isovel model (RIM) and field observations. We used the RIM to calculate the distribution of shear stress across an idealized trapezoidal channel and found that the ratio of wall to bed stress ( ) grows slightly with increasing bank angles, but exponentially declines with increasing width‐to‐depth ratio. We applied the RIM to 26 of the canyons along the Fraser River and found that RIM predicts 0.60 0.98. We used field observations of bed and total stress to calculate wall stress in each canyon. The total stress was calculated from 1D momentum balance (depth‐slope product). The distribution of bed stress was calculated from near‐bed velocity profiles and showed that bed stress spikes as water enters constriction‐pool‐widening (CPW) sequences. For the majority of studied canyons, the observed wall stress is larger than the total stress and the observed bed stress. The maximum observed bed stress through a CPW sequence is ∼7.5 times the mean bed stress and ∼4.9 times the total stress. Compared with the observed stresses, the model systematically over‐predicts the observed bed stress and under‐predicts the observed wall stress by ∼55%. Our results reveal that the complex flow structure in bedrock canyons influences the distribution of bed and wall stresses and that bedrock walls contribute more hydraulic roughness than predicted with RIM.
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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 | 0.001 | 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 | 0.001 |
| 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 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".