Bibliographic record
Abstract
ABSTRACT A lack of reliable hydraulic parameters has been a main factor hindering the progress in predicting ice‐covered river flows; the predictions need input hydraulic parameters such as the energy, momentum and drag coefficients (α, β and CD). In this paper, a large volume of winter measurements of flow velocity collected from 26 ice‐covered rivers is analysed to determine the coefficients. Using cross‐sectionally distributed streamwise velocities, α and β are evaluated directly. They are also derived from empirical relationships. For both the riverbed and ice cover, CD is evaluated on the basis of turbulent boundary‐layer theory and the quadratic law for friction. The results show that ice‐covered river flows feature a number of velocity distributions: a single core of high velocities in the thalweg, a single core of high velocities off the thalweg and multiple cores of relatively high velocities at the cross section. The velocity distributions are significantly non‐uniform. Direct evaluations give overall averages of α = 1.23 and β = 1.08. They represent 22% and 8.3% corrections to the literature values (overestimates). An examination of the velocity distributions reveals that the ratio of the maximum velocity to the cross‐sectionally averaged velocity equals 1.356. It is recommended that values of CD = 0.004 ± 0.0005 and 0.002 ± 0.0005 be used for the riverbed and ice, respectively. This paper discusses turbulence shear stress and the associated length scale in the boundary layer as well as winter discharges. The results have applications to aquatic ecology, water resources development and flood prevention. Copyright © 2012 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 | 0.001 | 0.001 |
| 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 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".