The Abundance and Persistence of Plunging Flows in Bedrock Canyons
Bibliographic record
Abstract
Abstract Landscape scale bedrock erosion is the integration of bedrock erosion at the reach scale, which is driven by particle impacts from sediment transport caused by near‐bed hydraulics. Plunging flow hydraulics have been identified in bedrock canyons and cause velocity profile inversions, which enhance near‐bed velocities, sediment transport, and the potential for bedrock erosion. Observations of plunging flows are limited, and the frequency and statistical properties of this hydraulic phenomenon have not been investigated. Here, we define metrics to identify velocity inversions and use them to detect instances of plunging flows through a 375 km reach of the Fraser River where channel morphology is controlled by bedrock. Isolated plunging flows are identified as well as plunging flow complexes where a series of plunges cause the core of maximum velocity to remain depressed in the water column for a prolonged distance. A significant relationship between plunging flows and bedrock exposure is identified, and plunging flows occupy more than half of the bedrock confined reaches. Stronger plunging flows are correlated with deeper and narrower channels with higher maximum shear stresses. Plunging flows are also concentrated in steeper reaches, which likely represent knickzones in the river profile. We use particle abrasion‐based bedrock erosion models to show that plunging flows drive reach‐scale incisions in bedrock rivers, creating deep bedrock pools. These pools dominate the incision into the bedrock, which sets the base level for their drainage areas and in turn sets the pace of landscape evolution.
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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".