The Temporal and Spatial Dynamics of Trace Metals in Sediments of a Highly Urbanized Watershed
Bibliographic record
Abstract
Abstract The Brunette watershed (7200 ha) is located in the urbanized metropolitan area of Greater Vancouver, Canada. It is an area of high traffic density and extensive impervious surfaces (paved roads and roof tops). This watershed provides an excellent area for the study of the spatial and temporal trace metal contamination in sediments. Street surface, stream, and lake sediments were collected over a 25-year period and analyzed for total and acid-extractable trace metals (Cu, Fe, Mn, Pb, and Zn). Lead concentrations in all areas have decreased dramatically, directly as a result of the discontinuation of lead addition to fuels in the 1970s. The mean concentration of total lead in stream sediments has decreased from 230 in 1973 to 134 and 36–66 mg/kg in 1993 and 1997–1998, respectively. Manganese, especially the acid extractable fraction, increased during the early 1990s when MMT replaced tetraethyl lead as an antiknock compound. The 0.5 M HCl extractable manganese in stream sediments has increased from 18 in 1973 to 545 in 1993 and 162–273 mg/kg in 1997–1998. Burnaby Lake, a shallow (Zav = 1.0 m, 140 ha) lake, has acted as a sink for trace metal contaminated sediments. Highest trace metal levels are found in surface sediments at the east end of the lake (where Cu, Pb, and Zn were 159, 179, and 529 mg/kg) containing more silt (24%) and higher organic matter (32.5%). The sandy delta of Still Creek (silt<4%, organic matter 5.6%), which contributes over 50% of the flow to the lake, has lower trace metal levels (Cu, Pb, and Zn were 72, 77, and 207 mg/kg) even though the creek is the predominant source of trace metals transported to the lake.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".