Estimation of composite hydraulic resistance in ice‐covered alluvial streams
Bibliographic record
Abstract
Abstract Formation, propagation, and recession of ice cover introduce a dynamic boundary layer to the top of rivers during northern winters. Ice cover affects water velocity magnitude and distribution, water level and consequently conveyance capacity of the river. In this research, total resistance, i.e., “composite resistance,” is studied for a 4 month period including stable ice cover, breakup, and open water stages in Lower Nelson River (LNR), northern Manitoba, Canada. Flow and ice characteristics such as water velocity and depth and ice thickness and condition were measured continuously using acoustic techniques. An Acoustic Doppler Current Profiler (ADCP) and Shallow Water Ice Profiling Sonar (SWIPS) were installed simultaneously on a bottom mount and deployed for this purpose. Total resistance to the flow and boundary roughness are estimated using measured bulk hydraulic parameters. A novel method is developed to calculate composite resistance directly from measured under ice velocity profiles. The results of this method are compared to the measured total resistance and to the calculated composite resistance using formulae available in literature. The new technique is demonstrated to compare favorably to measured total resistance and to outperform previously available methods.
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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 | 0.000 |
| 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 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".