Water Quality Transit Time (WQTT) for Continuous Velocity/Discharge Measurement in Large and Small Waterways
Bibliographic record
Abstract
Non-contact and automated flow measurement in open channels is becoming more popular as techniques improve to measure surface velocity, reducing costs and risk to hydrographers. However, these methods rely on estimates of bulk-to surface ratio estimates, as well as channel wetted area. This study considers the accuracy and application of paired Up and Downstream Water Quality (WQ) measurements to estimate the Transit Time (TT) and average bulk velocity. Combined with results from both the Automated Salt Dilution (AutoSalt) and Water Quality Mixing Model (WQMM) systems, we can calibrate the waterway for wetted area at a given water level, and hence estimate discharge from transit time velocity on a continuous basis using only temperature and conductivity insitu sensors. This low-cost method can used to build or validate rating curves, measure peak and low flow events, and conduct cost-effective hydrological assessments over large regions for any size waterway, to support climate change study and adaptation. This method also has application to flood wave propagation and Initial Dilution Zone (IDZ) studies. Results from a large and a small waterway, along with uncertainty, is discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".