Developing winter flow rating relationships using slope‐area hydraulics
Bibliographic record
Abstract
Abstract Despite recent advances in instrumentation and modelling methods, accurate determination of river discharge under an ice cover still requires direct measurement. Published flows at hydrometric gauging stations are based on interpolation between a few measurements that are carried out during each winter. The feasibility of using slope‐area hydraulics to develop discharge‐stage rating relationships is explored at two stations, Peace River at Peace Point and Mackenzie River at Arctic Red River. Records at both gauges contain key information for understanding local ice jamming processes, which are known to control the long‐term maintenance of the aquatic ecosystems in the respective deltas. For each site, the variations of reach‐average hydraulic parameters with stage are first determined from several nearby cross‐sections. This information is then used to calculate hydraulic resistance characteristics during the ice season based on archived discharge measurement data, which also include ice cover thickness. The Peace River flow measurements indicate a well‐defined seasonal variation in hydraulic resistance, with the exception of years with large slush deposits under the solid‐ice sheet. Slush effects are negligible at the Mackenzie River gauge site, but the stage‐flow relationship is complicated by a variable water surface slope, a result of downstream control by the Beaufort Sea. This feature is most pronounced during the pre‐breakup period when flows are rising sharply and renders flow estimation uncertain. A nearby water‐level gauge would help quantify the slope and increase confidence in winter flow estimates. Copyright © 2010 in the right of Canada.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".