Phytostabilization Potential of<i>Jatropha Curcas</i>L. in Polymetallic Acid Mine Tailings
Bibliographic record
Abstract
Greenhouse pot experiments were conducted to determine the growth response, metal tolerance, and phytostabilization potential of Jatropha curcas L The plants were grown on different degrees of multi-metal contaminated acid mine soils (T0, control; T1, moderately and T2, highly contaminated soils) with or without limestone amendments. The order of metal accumulation in J. curcas was roots>stems>leaves. The higher tolerance index (>90%) with no phytotoxic symptoms and growth reduction in T1 showed that this plant has the ability to tolerate polymetallic acid mine tailings. Further, various enzymatic and non-enzymatic antioxidants also actively involved in metal defense mechanism in J. curcas. On the other hand, to alleviate the predominant phytoavailable toxic metals such as Al, Cu, and Pb, different rates (0.1, 0.25, 0.50, and 1%) of limestone amendments were added in both T1 and T2 soils. The growth performance of J. curcas was improved due to the increase in soil pH and decrease in phytoavailable soil A1 (95%), Zn (approximately 75%), and Cu (approximately 65%) contents at 0.50% of lime addition. Based on the inherent tolerance ability of J. curcas in existing adverse environmental conditions without liming, it could be used as a suitable candidate for phytostabilization in acid mine tailings.
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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".