Seymour-Capilano Filtration Project: Transient Analysis Field Testing
Bibliographic record
Abstract
Home to over 2.4 million people, Metro Vancouver is responsible for delivering a variety of services and policy leadership for its members, which comprise 21 municipalities, one electoral area and one treaty First Nation. Among these services is the provision of potable (drinking) water. Operated by Metro Vancouver, the Capilano Raw Water Pumping Station in North Vancouver, British Columbia, is one of the largest municipal water pumping stations in North America with eight 2,000-Hp pumps and a capacity of 285 MGD (1,080 ML/d). This facility helps to ensure a reliable and safe supply to one of the largest cities in Canada. In support of commissioning the pumping station, a comprehensive transient analysis was carried out to mitigate potentially damaging water hammer conditions. As part of the transient analysis, a detailed field testing program was developed and conducted to confirm model results and evaluate the effectiveness of the surge mitigation measures. The field testing plan identified preferred data logger locations for the tests and identified additional information needed from the computerized data acquisition and control systems. A series of hydraulic transient field tests were performed over a period of three days. Field data results were then compared to model results for validation of the model. This paper will review: the development of the field testing plan; the decision making process for the placement of high speed pressure data loggers; level of coordination needed to complete the field tests; and a comparison of model versus field results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".