Spatial variability of trace metals in Podzols of northern forest ecosystems: Sampling implications
Bibliographic record
Abstract
The concentrations of metals in soils are spatially heterogeneous and soil sampling carried out when studying metals in forest ecosystems is often inadequate. We examined the spatial variability of Cd, Cu, Mn, Ni, Pb and Zn in northern forest ecosystems on a transect with distance from two point source emitters with the goal of providing basic information about the distribution and variability of metals in these soils. Samples were taken under six sampling points at four depths from three experimental sites on each of the two transects. Soils were analysed for acid digestible metals, pH and organic carbon content. Standard deviation, coefficients of variation, mean and medians were analysed for each horizon of each site. It was observed that Cu and Mn variability is higher in closer proximity to the point source while the variability of Zn increased with decreasing soil pH. In Sudbury, emitted metals Ni and Cu accumulate together in soils, whereas in Rouyn-Noranda, with the exception of Cd and Zn, metal concentrations were not related in forest floors. Observations suggest that physical factors influencing where metals are deposited as well as the inherent variability in soil chemical characteristics, and the distance from the point source can all act together to result in high variability in soil metal concentrations in a single forest site. Sampling in the interest of relating vegetation metal concentrations or response to soil metals should be carried out on an individual plant basis with multiple samples taken for each individual. Soil samples taken at intervals of 1 to 1.7 m will provide relative error in estimating soil concentrations of 10% or 20%, respectively. Key words: Spatial variability, trace metals, podzolic soils, smelter emissions
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".