Bioaccumulation of chemical elements in vegetables as influenced by application frequency of municipal solid waste compost
Bibliographic record
Abstract
Municipal solid waste (MSW) compost is used to enrich soils by virtue of its bio-physicochemical properties. However, undesirable accumulation of chemical elements can reduce soil quality and cause food safety issues. A 5-yr field study was carried out to investigate the impact of Compost Quality Alliance (CQA)-tested MSW compost application frequency (annual, biennial and no-compost) on soil quality and chemical element accumulation in edible portions of lettuce (Lactuca sativa cv. Grand Rapids), beet (Beta vulgaris cv. Detroit Supreme), carrot (Daucus carota cv. Nantes), and green bean (Phaseolus vulgaris cv. Golden Wax). Analysis of soil showed that chemical elements were highest in annual application followed by biennial, but less in control (no-compost) and fallow soils. Soil background levels of chemical elements influenced the concentrations of iron (Fe) and manganese (Mn) in green bean, aluminum (Al) in green bean and beet, and barium (Ba) in carrot, beet, and lettuce. Cadmium (Cd) concentration in beet, lettuce, and green bean grown in the annual plot was increased by 48%, 52% and 62%, respectively while carrot recorded a 56% increase in the biennial plot compared with no-compost. Bioaccumulation factors were < 1 for all of the essential and non-essential trace elements in all of the plant species, except boron (B) and molybdenum (Mo). However, lettuce showed a higher tendency to accumulate Cd, rubidium (Rb), and strontium (Sr). Overall, the health risk for human consumption is low. Although long-term annual application of compost to vegetables seemed safe for human consumption, it is necessary to continuously monitor potential chemical element accumulation, particularly non-essential trace elements in soils and plants.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".