Toxicity and metal bioaccumulation in <i>Hordeum vulgare</i> exposed to leached and nonleached copper amended soils
Bibliographic record
Abstract
Soil leaching has been proposed as a way to reduce soil-spiking artifacts (i.e., increased acidity, metal solubility) that occur in soils amended with trace metal salts. Leaching metal-spiked samples prior to ecotoxicity testing is therefore expected to reduce toxicity; however, leaching not only removes excess amounts of the trace metal being tested, but also reduces the concentrations of cations that could decrease the toxic effects of the metal of interest. To clarify these conflicting processes, the effects of leaching on toxicity and bioaccumulation of Cu, Ca, and Al were investigated using 14-d plant assays conducted on leached and nonleached, Cu-spiked soils. The median effective concentration (EC50) to root elongation ranged from 78 µg/g to 589 µg/g. Leaching was found to reduce toxicity by 1.2-fold to 2.1-fold. The Cu(2+) activity predicted toxicity better than root or shoot Cu concentrations, which were generally not affected by leaching. Plant uptake of Ca increased with increasing Cu dose in nonleached samples, which likely contributed to the hormesis-like response observed in these samples, whereas Ca uptake in the leached samples was more consistent with that of the control except at the largest Cu doses for which Ca uptake decreased. Surprisingly, Al uptake in the most acidic soil was greater in leached than nonleached samples, which may have contributed to the greater toxicity exhibited in this soil than was predicted by Cu(2+) activity. These findings have implications for predicting trace metal toxicity in nutrient-stressed, acidic soils. Environ Toxicol Chem 2013;32:1800-1809. © 2013 SETAC.
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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) | 0.001 | 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) | 0.001 | 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".