Bibliographic record
Abstract
The Greater Vancouver Regional District (GVRD) supplies water to 1.9 million people from three coastal mountain watersheds. Water quality concerns identified include waterborne disease risks associated with Giardia and Cryptosporidium, episodes of elevated turbidity, bacterial regrowth in the distribution system, and corrosive water. To address these concerns, the GVRD has initiated a $300 million capital program to upgrade its treatment capabilities that includes two 1200 ML·d1 ozone and corrosion control facilities, a 1000 ML·d1 filtration plant, and an ongoing program of rechlorination stations and distribution system improvements. This paper provides an overview of the GVRD's drinking water treatment program and related initiatives. These include the decision-making process related to using ozone without filtration, the process selection and selected project delivery method for the Seymour filtration plant, and, lastly, the ongoing secondary disinfection program which includes unidirectional flushing, reservoir exercising, environmental management, and remote data monitoring and data evaluation using a geographical information system application.Key words: water quality, protozoa, ozone, direct filtration, rechlorination, remote monitoring, GIS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".