Toxic elements and heavy metals concentrations in playground of elementary schools of Brandon, Manitoba, Canada
Bibliographic record
Abstract
Chemical safety of school playgrounds is very important for the health and well-being of students and it is expected that level of heavy metals [including toxic elements] be below acceptable [non-toxic] levels. The risk from accidental consumption of potentially toxic elements in playground soils is of concern to children’s health. Concentration of heavy metals is a very important factor in assigning type of land use. For example, Canadian Council of Ministers of the Environment [CCME] has published soil quality guidelines to help protect environmental and human health. These guidelines identify 4 types of land use in Canada: agricultural, residential/parkland, commercial, and industrial. Different concentrations of heavy metals are suggested for each land use. A total of 73 samples were collected from playgrounds of 16 elementary schools in Brandon, Canada and analysed for major and light elements [Fe, Ti, Ca, K, Al, P, Si, Cl, S, Mg] as well as trace and heavy metals [As, Ba, Cd, Cr, Cu, Pb, Mn, Ni, Se, U, V, Zn]. The elemental relationships in these samples show that the data sets were predominantly influenced by natural geologic element dispersion and accumulation processes. Anthropogenic sources generally enrich individual [or small number of] elements and, as a result, distributions are disturbed and skewed towards higher values in natural systems. Lower values are commonly free from anthropogenic influences. This research shows that As, Ba and Cu are the only heavy metals in playgrounds of some schools in Brandon that are higher than, or close to, the values suggested by the Canadian soil quality guidelines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".