Multi-metal bioaccumulation ability of <i>Sesuvium portulacastrum</i> modulated by salinity in a high metal chelating substrate
Bibliographic record
Abstract
Halophytes are known to be potential phytoremediators of metal-contaminated saline soils. Sesuvium portulacastrum (L.) L., has been reported to accumulate metals and is a candidate for metal decontamination in saline conditions. Here, we aimed to assess the impact of salinity on the ability of S. postulacastrum present in New Caledonian estuaries to extract multiple metals under conditions that facilitate the understanding of the plant’s extraction effectiveness. S. portulacastrum was cultivated in vermiculite and was watered twice a week with various concentrations of NaCl alone or combined with two concentrations (M1 and M2) of a multi-metal mix containing Ni, Co, Cr, and Mn. The results showed that shoot growth was at its maximum in the presence of 0 and 200 mM NaCl. At this last salt concentration, the M1 metal level in the substrate had no significant effect, but M2 decreased plant growth drastically. However, the total metal accumulation in shoots and roots was the highest under M2 multi-metal exposure. S. portulacastrum accumulated relatively high levels of metals in roots under varying salinity conditions. S. portulacastrum from the estuaries of New Caledonia exhibits significant potential for the phytostabilization of metals, even as salinity and metal concentrations increase in its growth areas.
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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".