Stress Response and Phytoextraction Potential of Two Noccaea caerulescens Populations in Multicontaminated Soil
Bibliographic record
Abstract
Abstract Background and Aims Multi-contamination of soils by various organic and inorganic pollutants is considered an obstacle for the development of hyperaccumulator plants and phytoextraction of metals. The aim of this study was to investigate the effect of polycyclic aromatic hydrocarbons (PAHs) in combination with trace elements on the antioxidant response and phytoextraction efficiency of the Ganges and Chavignée populations of the hyperaccumulator Noccaea caerulescens.Methods Plants were grown in soil containing some heavy metals at moderate concentrations under phenanthrene (PHE), a model PAH stress condition, for 17 days.Results In general, exposure to PHE resulted in a reduction of growth parameters, along with the upregulation of antioxidant enzymes and compounds and limitations in nutrient uptake and heavy metal extraction in N. caerulescens. Variations were observed in the magnitude of enzymatic activities and the amount of extracted metals between the two studied populations. Chavignée plants exhibited a slightly more tolerant response to stress than Ganges.Conclusion The presence of PHE in the soil proved to be highly toxic for N. caerulescens. Nevertheless, to some extent, growth, metals extraction, and antioxidant defense responses differed slightly between the studied populations, suggesting that the difference in defense capacity might ensue different tolerance. This distinction may be related to the adaptations acquired by each population depending on the soil type it originated from.
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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) | 0.001 | 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".