Automated Salt Dilution Instream Q (ASDIQ) with Image Velocimetry (IV): It Looks Like a Salty Marriage
Bibliographic record
Abstract
Salt Dilution (SD) is an accurate, safe, relatively inexpensive and easily employed method to measure water flow in turbulent streams and rivers. It has been used in some form for over 100 years and continues to experience a renaissance with refined methods and improved technologies. However.. SD is challenging in less turbulent flows without “complete” lateral mixing, and also requires a continuous estimate of water level or other proxy to generate a continuous hydrograph. Image Velocimetry (IV, Large Particle IV or Space Time IV), on the other hand, has been used for more than 20 years to estimate the flow in more placid rivers and streams without making contact with the water, using high resolution video to measure the surface velocity. However.. apriori estimates of the surface (VS) to bulk (VB) velocity ratio (k) is required, as well as the channel wetted area (A).In this research we examine whether we can marry the two technologies to create a comprehensive automated flow measurement system to span all flow regimes from turbulent to placid, by removing the need for apriori knowledge in the case of IV, and using continuous imagery as the proxy flow estimate in the case of SD. SD measures Q; IV measures VS; this method combines the two using the equation Q = VS*k*A to estimate continuous Q, as well as an estimate of surface to bulk velocity ratio (k), and wetted area (A).The method/system has the potential to replace conventional stations that rely on expensive and dangerous site visits and error prone water level sensor proxies. The results of our preliminary investigations are presented for 3 test stations.
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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 | 0.002 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".