1 Investigation of the Performance of Acoustic Scintillation Flow Meter when Turbulence Levels are Low
Bibliographic record
Abstract
Abstract – Electricité de France (EDF) is funding a 3-year PhD work on acoustic scintillation flow metering, in association with Hydro-Québec and ASL AQFlow. The PhD project aims at improving the discharge estimation when hydraulic conditions are not ideal for acoustic scintillation measurements, as well as providing a better understanding of the effects of sources of interference which can be encountered during the measurement process and can cause inaccuracies in the velocity estimation. In order to achieve these objectives, a fast and portable data acquisition system was set up, which relies on high speed acquisition cards. Each four channel acquisition card can be connected in parallel with up to three others, thus forming a high speed multi channel data acquisition (DAQ) system. A first test of this DAQ system was performed at one of Hydro-Québec's hydroelectric plant. As the ASFM replaced the existing trash rack elements, special equipment was designed and built to create turbulence in the flow necessary for the acoustic scintillation to operate. HPP performance tests by Hydro Quebec were duplicated with the EDF measurement system which recorded the acoustic scintillation raw signals. Using the resources available at the time, the acquired time series have a lower resolution than those provided by the Acoustic Scintillation Flow Meter (ASFM). However, valid velocity estimates are possible even at this lower resolution. I.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".