Lithium, Vanadium and Chromium Uptake Ability of<i>Brassica juncea</i>from Lithium Mine Tailings
Bibliographic record
Abstract
The potential for phytoremediation and phytostabilization of lithium in lieu with vanadium and chromium on a formulated acidic heterogeneous growth media engineered around lithium mine tailings, was investigated in four phases: (1) overall efficiency of the removal of the three metals, (2) bioaccumulation ratios of the three metals, (3) overall relative growth rate, and (4) translocation index of the three metals in the physiology of the hyperaccumulator plant. A pot study was conducted to assess the suitability of Brassica juncea (Indian mustard) in a phytoremediation process whereby it was lingered for eighty-six days under homogeneous growth conditions and irrigated bidaily with organic fertilizer amended with LiCl. A post harvest data analysis was achieved through ashing and the implementation of cold digestion procedure in a concentrated hydrochloric acidic matrix. In physiological efficiency parameters, the hyperaccumulator plant was twice as able to phytostabilize chromium and four times was able to phytostabilize vanadium in comparison to lithium. Moreover, it was extremely efficient in translocating and accumulating lithium inside its upper physiological sites, more so than chromium and vanadium, thereby demonstrating Indian mustard, as a hyperaccumulator plant, for phytoextraction and phytostabilization in an acidic heterogeneous rhizosphere, with an extremely low relative growth rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".