Bibliographic record
Abstract
In the Greater Vancouver Regional District (GVRD), non-point source pollution from an urban watershed and a diesel bus loop was assessed in terms of trace metal contamination in the stormwater runoff. In the Brunette River watershed study, Northwest Hydraulic Consultants (NHC) collected streambed sediment and suspended sediment from selected streams during 7 storm events over 2003. From 1993 to 2003, the major stormwater contamination happened in the most industrialized Still Creek. The streambed Cu, Mn, Fe, and Zn concentration increased by 1.5, 1.7, 1.9, and 1.1 times, respectively. And the suspended Cu, Mn, and Zn increased by a factor of 2.1, 4.2, and 1.5, respectively. The streambed sediment exceeded probable effect level in Still Creek and Stoney Creek to varying degrees with Cu and Zn. The land use is considered to be the origins of these toxicants. Statistically, the magnitude of suspended metal concentration in μg/l is negatively correlated with the drainage areas. While the — concentrations in mg/kg, especially for metal Cu and Zn, showed strongly and positively correlation with the traffic density. Positive correlation existed between the suspended metal loading (kg/yr) and the imperviousness and the catchment area. No apparent trend was observed in terms of export coefficient (g/ha/yr) and land use. 1062 tons of sediments were trapped by Burnaby Lake in 2003. This sediment overloading problem causes serious metal contamination in the lake. Stormwater runoff quality was monitored in 15 storm events from October 2004 to June 2005 in the diesel bus loop in the University of British Columbia. The dissolved Cu and Zn Event Mean Concentration (EMC) exceeded the EPA discharge criteria in 2 and 4 events each, which occurred in the dry season. Diesel bus traffic contributes' a large portion of Cu, Fe, Zn contamination since the average bus loop trace metal levels were much higher than the GVRD urban levels. The runoff trace metal concentrations are strongly related to the antecedent dry period, and are weakly related to the traffic density and the rainfall intensity. From the catch basin filter evaluation, high removal efficiencies on suspended metal/solids were achieved with low particulate loading in the filter chamber. The filter performed well for the dissolved metal removal before the non-reversible saturation was reached. Each kilogram of filter media has an absorption capacity of 52 gram oil and grease, 20 milligram Mn, and 16 milligram Zn.
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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 | 0.000 |
| 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".