Distribution and In Vitro Bioaccessibility of Potentially Toxic Metals in Soils at Select Urban Parks at Eastern Canadian Cities
Bibliographic record
Abstract
This study investigated the human health risks associated with exposure to potentially toxic metals, including arsenic, barium, cadmium, chromium, cobalt, copper, lead, nickel, and zinc, at select parks in Eastern Canadian cities. Except for arsenic in Halifax, the mean metal concentrations in the cities, including Saint John, Fredericton, Ottawa, Toronto, London, Windsor, Woodstock, Kitchener, Guelph, Chatham, and Montreal, were below the Canadian Council of Ministers of Environment soil quality guideline for parkland use. Metal distribution reflected either the regional natural-occurring concentrations or anthropogenic sources such as industrial activities, historical land use, and heavy traffic corridors. In vitro bioaccessibility values were variable and in the order chromium < nickel < cobalt < arsenic < zinc < copper < lead < cadmium. The risk associated with incidental soil ingestion for children, incorporating bioaccessibility, indicated unacceptable levels of non-carcinogenic effects for 6 out of the 101 samples analyzed. For adults, unacceptable non-carcinogenic effects were noted for only one sample. Lead was the leading contributor to the non-carcinogenic risk. Carcinogenic risk for arsenic was limited to two samples. The overall risks associated with exposure to metals in soils in most of the parks studied were deemed low except for arsenic and lead at a few parks.
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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 | 0.002 | 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".