Hydro 2002, Kiris, Turkey Recent advances in estimating uncertainties in discharge measurements with the ASFM
Bibliographic record
Abstract
This paper concentrates on the present understanding of the accuracy of the ASFM, both in terms of the systematic and random uncertainties. The described progress achieved since the initial paper on the ASFM uncertainties was published at Hydro 2001 is based on the results of direct field measurements of turbulent intensities and temperatures in the boundary zones, precise measurements of transducer spacings, and repeat discharge measurements. Further work required to achieve and confirm the accuracies demanded by the hydroelectric industry is also outlined. 1. ASFM operation Traditional discharge measurement methods such as current meters or the more recently introduced timeof -flight acoustic flow meters, have been and continue to be used for measuring turbine discharges at short intakes of low-head plants. Their continued use, in spite of significant practical difficulties (introduction of obstructions into the flow, intensive labour requirements, including even the necessity of dewatering the intake, and major interference with power generation), clearly demonstrates that a more efficient discharge measurement tool is needed. This tool should be at least as accurate as those available today, but faster, easier and cheaper to use. In addressing this need, over the last 10 years ASL Environmental Sciences, and more recently a subsidiary company, ASL AQFlow Inc., both of Sidney, British Columbia, Canada, have developed the Acoustic Scintillation Flow Meter (ASFM). The ASFM utilizes the natural turbulence embedded in the flow, as shown in Fig. 1. Two transmitters are placed on one side of the intake, two receivers at the other. The signal amplitude at the receivers varies randomly as the turbulence along the propagation paths changes with time and the flow. If the two ...
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.002 |
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".