Effects of Ectomycorrhizal Fungi and Heavy Metals (Pb, Zn, and Cd) on Growth and Mineral Nutrition of Pinus halepensis Seedlings in North Africa
Bibliographic record
Abstract
The pollution of soils by heavy metals resulting from mining activities is one of the major environmental problems in North Africa. Mycorrhizoremediation using mycorrhizal fungi and adapted plant species is emerging as one of the most innovative methods to remediate heavy metal pollution. This study aims to assess the growth and the nutritional status of ectomycorrhizal Pinus halepensis seedlings subjected to high concentrations of Pb, Zn, and Cd for possible integration in the restoration of heavy metals contaminated sites. Ectomycorrhizal and non-ectomycorrhizal P. halepensis seedlings were grown in uncontaminated (control) and contaminated soils for 12 months. Growth, mineral nutrition, and heavy metal content were assessed. Results showed that ectomycorrhizae significantly improved shoot and roots dry masses of P. halepensis seedlings, as well as nitrogen shoot content. The absorption of Pb, Zn, and Cd was much higher in the roots than in the shoots, and significantly more pronounced in ectomycorrhizal seedlings—especially for Zn and Cd. The presence of ectomycorrhizae significantly reduced the translocation factor of Zn and Cd and bioaccumulation factor of Pb and Cd, which enhanced the phytostabilizing potential of P. halepensis seedlings. These results support the use of ectomycorrhizal P. halepensis in the remediation of heavy metal contaminated sites.
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) | 0.001 | 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 | 0.001 |
| 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".