The utilization of <b><i>Ricinus communis</i></b> in the phytomanagement of heavy metal contaminated soils
Bibliographic record
Abstract
Soil contamination with toxic metals is a major global concern due to their effects on plants and the ecosystem. In contaminated soils, some plant species have the ability to remediate heavy metals. Ricinus communis L., is an industrial crop plant gaining popularity in the remediation of heavy metal contaminated soils owing to its strong and deep penetrating roots aiding high metal accumulation and large biomass level. Ricinus communis can tolerate high amounts of metals by adopting different strategies, which include the production of antioxidant enzymes, subcellular localization, and exudation of organic acid. At the molecular level, R. communis can tolerate metal stress by activating stress-responsive genes. Proper selection of metal-tolerant R. communis cultivars is effective in the remediation of metal-contaminated soils, owing to their high capacity for metal tolerance. Exogenous application of mineral fertilization and the use of microbes and chelating agents increase metal solubility and availability for plant uptake in soil. Also, good agronomic practices such as co-planting of R. communis with other leguminous crops enhance R. communis growth and metal tolerance, thereby improving remediation of metal-contaminated soils. This review, therefore, critically discusses the recent approaches in using R. communis to remediate metal-contaminated soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".