Development and testing of a modified transparent velocity-head rod for stream discharge measurements
Bibliographic record
Abstract
Accurate measurements of streamflow velocity and discharge can be challenging due to the technical nature of the instruments and often-associated high cost. Velocity-head rods measure the head difference in water flowing around an obstacle (i.e. the head rod) and use hydraulic theory to estimate water velocity. This article presents the construction and testing of a modified transparent velocity-head rod (mTVHR). Approximately 2400 pairs of mTVHR and SonTek FlowTracker (FT, acoustic Doppler) measurements were made on 31 dates, by seven operators at 14 sites. This data set allowed for the development of a calibrated relationship for velocity, for the investigation of accuracy among multiple users and for the definition of stream conditions where mTVHR use is appropriate. While there was evidence of differences between users for measurements of velocity-head and water depth, these differences were small and did not greatly influence discharge estimates derived using the instrument. While statistical analysis determined a minimum measureable velocity-head value of 1 mm, field experience with the instrument indicates the mTVHR should be used to measure velocity-head values greater than 2 mm. The maximum water depth recommended is approximately 0.75 m due to the 1-m height of the instrument and the difficulty of safe operation at moderately fast streamflow velocities. Turbulent streams and/or streams with large bed materials are not appropriate for use of the mTVHR due to rapid velocity-head fluctuations. The mTVHR is an accurate instrument for measuring streamflow velocity, depth and discharge. In addition, its ease of use, low cost (< CAN $100), and durability make it an attractive option for streamflow measurement.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".