Effect of Amendments on Phytoavailability and Fractionation of Copper and Zinc in a Contaminated Soil
Bibliographic record
Abstract
The ability of amendments to modify the soil properties and influence plants to immobilise Cu and Zn was studied in a naturally contaminated, additionally spiked podzolic soil. Lolium perenne L (perennial rye grass), Festuca rubra L (creeping red fescue) and Poa pratensis L (Kentucky blue grass) were tested in a pot study in the presence of soil amendments (lime, phosphate, and compost, individually and in combination) to assess the effect of soil-plant-amendment interaction on phytostabilisation. The ability of treatments to stabilize metals was assessed on the basis of metal fractionation in soil, partitioning of metals in plants, and metal uptake by the plants. Significant partitioning of Cu into immobile forms occurred as a result of the growth of Festuca rubra, and of Zn by the growth of Poa pratensis. Application of lime significantly reduced the exchangeable fraction of Zn, whereas phosphate application had an accelerating effect on exchangeable Cu. With combined application of amendments, the plant metal concentration decreased by more than 40% for Cu and 70% for Zn, compared to soils receiving no amendments. Combined application of amendments, in conjunction with growth of Festuca and Poa, can be recommended for phytostabilising of Cu and Zn in moderately contaminated acid soils of southwest British Columbia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".