Bibliographic record
Abstract
The decision to upgrade the Annacis and Lulu Island sewage treatment plants in the Greater Vancouver Regional District (GVRD) was analysed in light of uncertainty regarding future population growth and inputs from industrial and urban runoff sources. Sustainability is often cited as a reason for maintaining pristine water quality. However, there are several sustainability world views and they do not all necessarily advocate the maintenance of pristine water quality. Approaches to sustainability are reviewed and discussed in the context of water quality management. Methodology was developed to link discharges from industrial sources, urban runoff and sewage treatment plants to user defined inputs of economic activity, development and land-use patterns and population growth. The pollutant loading was then used to determine the water quality at various locations in the Fraser River Estuary. Inputs from industrial sources, urban runoff and sewage treatment plants upstream of the GVRD were assumed to be completely mixed at the sewage treatment plant outfalls and to affect ambient water quality. Local impacts from urban runoff, industrial discharges and upstream sewage treatment plants were not considered. The primary reason for considering these sources was to determine whether future levels of discharge are likely to have an effect on management decisions regarding municipal sewage treatment plants. Diffusion factors and dispersion coefficients have been determined for various locations in the Fraser River. These were adapted to determine the local impacts on water quality from future increases in sewage treatment plant discharge. The changes in ambient and local water quality were added to determine the overall water quality for each future scenario. The decision to upgrade the two treatment plants was discussed in the context of water quality criteria and sustainability world views.
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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".