Site-specific natural background concentrations of metals in topsoil from the Trail region, British Columbia, Canada
Bibliographic record
Abstract
The natural background level of elements in the surface soil of a given area is largely dependent on the geological setting of the region and the underlying soil parent material. Due to the heterogeneity of soil in various environments, it is essential to determine the site-specific natural background levels of elements to quantify the degree of influence of the anthropogenic source(s) within a given study area. This paper discusses the process involved in establishing the natural background level of trace metals and other elements in topsoil for the area surrounding the Teck Cominco zinc–lead smelter in Trail, British Columbia, Canada. The environmental assessment of soil in the Trail region is difficult due to the natural enrichment of metals in surficial material and topsoil as a result of the influence of mineralized bedrock in the region. Detailed examination of the study area based on the deposition data and previously collected soil data led to the selection of presumably the least anthropogenically influenced background sites in the study area, which also reflect the geological diversity of the region. The range of background was defined using the median plus or minus two median absolute deviations [median±(2 × MAD)], after the outliers were excluded. The outliers were identified using the Tukey boxplot concept and the cause of high metal concentrations in the outlier samples is attributed to their proximity to the historical mining sites in the region. The upper limit of the background range was estimated to be (in mg/kg): As, 11.0; Cd, 0.81; Cu, 38.1; Hg, 0.07; Pb, 27.7; Zn, 152. The resulting values are significantly more conservative estimates than would be obtained by other widely used methods (e.g. the mean plus or minus two standard deviations) due to the robust procedure, which prevents any interference by the extreme values in the collected soil background dataset. The upper background thresholds of metals estimated by the 95th percentile resulted in significantly different estimates, especially for As, Hg, Pb and Zn, from the previously reported values. This highlights the need for revision of the risk-based screening levels by environmental regulations in the study area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".