Bibliographic record
Abstract
At present, the only truly accurate method of determining discharge under ice-affected conditions is by direct measurement. However, this is a costly undertaking that places technicians at personal risk. Because of this, the Water Survey of Canada often bases winter discharge hydrographs on as few as two direct measurements over the entire ice-affected season. This study explores the viability of using an index velocity approach to winter discharge measurement, which basically involves the determination of streamflow based on a very limited number of actual point velocity measurements in the cross section. Such an approach has the potential to reduce the amount of time required for a single discharge measurement, thus opening the potential for an increase in the number of discharge measurements that can be obtained per year at a given site. Also, a reduction in personal risk to those taking measurements on the ice cover is expected because of the reduction in the time spent on the ice cover for each individual measurement. Here, the viability of using a single point velocity, or alternatively a single panel velocity profile, is explored using detailed velocity measurement data collected by the Water Survey of Canada at a number of sites across Canada, on rivers of varying size. Results suggest that there is a definite potential to streamline winter discharge measurement protocols to increase both safety and frequency of measurement.Key words: winter streamflow measurement, rivers, ice.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".