The effect of ice cover on velocity and shear stress in a riffle‐pool sequence
Bibliographic record
Abstract
Abstract Discharge and channel geometry control velocity and bed shear stress within a reach. Channel roughness (e.g., riffles, pools) and ice cover in winter moderates velocity and shear stress at the bed. This study evaluated velocity profiles across a channel segment with variable bed roughness (e.g., riffles, pools) to determine changes in the position of maximum velocity, maximum velocity magnitude, and resulting bed shear stress estimates when ice cover was present. Using acoustic doppler velocimeter (ADV) and acoustic doppler current profiler (ADCP) high resolution velocity profiles were collected during ice cover, open water, and open water with significant increases in vegetation cover through a riffle‐pool sequence in a low‐order channel in southern Ontario Canada in the first half of 2021. Key findings were that in five of the seven cross‐sections, flow direction was significantly different when ice was present. Additionally, maximum velocities were closer to the bed during ice cover, a common finding in modelling and experimental work, and is confirmed in this field setting. Although maximum velocity magnitudes were not significantly different, derived bed shear stress values under ice were larger. Specifically, under ice conditions, riffle bed shear stress ranged 0–16 N/m 2 compared to 0–9 N/m 2 in ice free conditions. In the pool, bed shear stress ranged 0–6 N/m 2 under ice cover, and 0–5 N/m 2 in ice free conditions. Further, as flow levels increased through the spring and summer, this coincided with increased in‐stream vegetation cover, which decreased flow velocities near the bed, and thus decreased bed shear stresses to less than 1 N/m 2 in both the riffle and pool sections. The findings indicate that channel evolution processes may be more intense during lower‐stage winter flows when ice is present and has significant implications for channel design, restoration and management strategies used in small channels impacted by ice cover.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".