Site- and Species-Specific Patterns of Metal Bioavailability in Edible Plants
Bibliographic record
Abstract
Differences in metal uptake between plant species and soil types were compared to assess the safe use of mildly contaminated soils for the growth of edible food crops. Accumulation of metals in five plant species grown in each of three field soils and a commercial soil were evaluated in a controlled environment room. Metal bioavailability varied more with plant species than with type of soil. Among a number of physical and chemical soil properties that were determined, high metal content and low percent organic matter were the best predictors of increased metal bioavailability. Contamination levels of metals measured in soil and vegetable samples were used to calculate bioconcentration factors and hazard quotients. The results indicated significant differences between plant species. The most metal-accumulating species was carrot and the most mobile element was cadmium. Some hazard quotients exceeded the threshold value of 1, even in soils considered uncontaminated by current guidelines. Overall, these results reinforce the need to include soil characteristics when setting threshold guidelines for metal content of agricultural soils and indicate the need for species-specific planting guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".