An evaluation of highway stormwater runoff quality in the G.V.R.D.
Bibliographic record
Abstract
In the Greater Vancouver Regional District (G.V.R.D.), highway stormwater runoff from bridge decks along the Trans-Canada Highway (#1) in Burnaby and the New Westminster Highway (#91, east-west connector) in Richmond was assessed between 1995 and 1996. Discrete and composite samples of highway stormwater runoff, road dirt, surface soil sediment and grass clipping samples were collected manually from both sites. The stormwater runoff samples were analysed for total suspended solids (T.S.S.), chromium (Cr), nickel (Ni), cadmium (Cd), copper (Cu), iron (Fe), zinc (Zn), manganese (Mn), lead (Pb), calcium (Ca), oil/grease, pH and electrical conductivity (EC). The road dirt, soil sediment and grass clipping samples were analysed only for their metal content. All the parameters in highway stormwater runoff showed differences in seasonal concentration patterns except for Cu and Mn at both sites. However, these differences were not statistically significant at the 95% confidence level. Although concentrations of most pollutants were higher in the winter, LC50 daphnia bioassays were non-toxic. The non-winter Comp "A" runoff samples, on the other hand, had 70% and 57% survival rates after 24 and 48 hours respectively. Most contaminant concentrations exceeded the maximum allowable concentrations (MAC) set for drinking water or freshwater aquatic life protection. Between the two sites, the Burnaby site had higher rainfall amounts and runoff coefficients, thereby generating higher T.S.S., metal and oil/grease concentrations/loadings than the Richmond site. The Burnaby site grass drainage ditch was fairly efficient in its pollutant removal effectiveness which ranged from 48% for Cu to 77% for T.S.S. There were statistically significant differences in pollutant removal efficiencies for all the parameters except for Mn at the 95% confidence level. Pollutant concentrations forecasting, using single regression equations with individual environmental variable, yielded reasonably good predictions for T.S.S., Fe, and Mn at the Burnaby site; and T.S.S., and Ca at the Richmond site. Comparison between discrete sample and flow composite data indicated a significant difference only in the concentration of T.S.S. at the Burnaby site.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".