Phytocycling of Cu, Ni, Pb and Zn in northern forest ecosystems impacted by smelter emissions
Bibliographic record
Abstract
Decomposition of forest plant material releases metals such as Cu, Ni, Pb and Zn, accumulated through uptake and/or through surface adsorption, into the soil environment where metal fate ranges from immediate system loss due to leaching, to long-term residency by complexation with organic and inorganic substrates. Mass estimates of phytoaccumulated trace metals and transfers to soil are necessary to properly evaluate the impact of historic and continued anthropogenic metal deposition to northern forest ecosystems subject to contamination by smelter emissions. Study site proximity to metal smelters in areas near Sudbury, ON and Rouyn-Noranda, QC, increased exposure of forest plants to both aerial and soil metal contaminants resulting in increased concentrations of these in plant tissues. Fine roots accounted for the majority of plant-accumulated Cu and Pb at the most heavily contaminated sites and dominated annual mass transfer (>90%) of these to soils at all study sites (contaminated and uncontaminated) where comparisons were made with foliage. Fine roots also played the more significant role in Ni transfer when soil concentrations of this metal were high (76% at Sud 1), and a reduced role when soil concentrations were low (26% at RN 3). The relative transfer of Zn to soils via foliage and fine roots was proportional to the amount of biomass turned-over in these two compartments, and therefore tended to be mainly transferred by foliage. Net increases in foliar litter metal contents were observed at both contaminated and uncontaminated sites after 18 months in the field, despite significant reductions in litter mass due to decomposition. In contrast, fine root mass losses appeared to be mirrored by proportional losses of metal contents at uncontaminated sites, but not at contaminated sites, where losses of metals occurred, but were lower.
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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 | 0.001 | 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".