Sustainable Infrastructure: Reconstruction of the Little Mountain Reservoir
Bibliographic record
Abstract
The Greater Vancouver Water District (GVWD) owns and operates the water transmission system that serves approximately 2 million residents in the Greater Vancouver area, located in British Columbia, Canada with an average water consumption of 1.2 million cu. m per day, making the system one of the largest in Canada. The Little Mountain Reservoir located in Queen Elizabeth Park, in the City of Vancouver is the largest and most important of the 22 reservoirs in the GVWD system. The reservoir was originally constructed as an open basin in 1910 and a roof structure was added in the mid 1960s. Because of seismic and structural deficiencies of the structure, it was decided to demolish the reservoir and construct a new one on the same site with increased capacity and enhanced operational flexibility. Demolition began in September 2002 and construction of the new facility was completed by December 2003 on schedule, within budget. This paper summarizes the options evaluated before deciding to reconstruct the reservoir and details of GVWD's seismic performance criteria. Design features of this 175 million litres (38.5 million gallons) capacity reservoir will be presented with the challenges and constructability issues faced by the project team in building a major infrastructure in an urban park setting. The paper will show how the principles of sustainability were applied effectively by addressing social, environmental and economic issues in a balanced manner.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".