An Installed Accuracy Assessment using Dye Dilution Testing for Seven Common Flow Metering Technologies
Bibliographic record
Abstract
An accurate dye dilution testing protocol using Rhodamine WT was developed and used to quantity flow meter accuracy in the Greater Detroit Regional Sewer System.Over 150 tests were performed on 3 7 flow meters in conjunction with a set of good metering practice principles.A summmy of the accuracy for each of the seven technologies tested before and after good metering practice is given.The seven technologies assessed are electromagnetic induction meters (magmeters); full-conduit, multiple-path, transit-time meters; full-conduit, single-path, transit -time meters; open-channel, multiple-path, transit-time meters; open-channel, ultrasonic meters; flumes; and weirs.Many meters that are typically considered to be accurate had enors of more than 30%.Some meters had en•ors that exceeded 70%.Overall, the average initial system accuracy for system meters was observed to be ±15.0% of measurement with an overall bias of 6.1% (underpredicting flow).After implementing the good metering practice principles, and in particular using dye dilution testing as a diagnostic tool, the average accuracy potential observed for the system reduced to ±5.5% of measurement, with an overall bias of 0.6% (underpredicting flow).It is concluded that (i) there are observable accuracy differences between flow meter technologies, (ii) objective standards like dye dilution testing are critical to good metering, (iii) verifYing installed accuracy is important, even for technologies considered to be highly accurate, and (iv) the simplest technology that can be used is often the best Stonehouse, M.C.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".