Phytoremediation of CR(VI) by <i>Spirodela polyrrhiza</i> (L.) Schleiden Employing Reducing and Chelating Agents
Bibliographic record
Abstract
Phytoremediation of Cr(VI) by Spirodela polyrrhiza in binary combinations with low molecular weight organic compounds (LMWOCs) with a reducing or chelating potential, viz., ascorbic acid, citric acid, tartaric acid, oxalic acid, lactic acid, and glycerol was studied in Cr(VI) containing hydroponic media. Significant increase in the relative dry weight of plants with respect to Cr(VI) treated controls was observed with ascorbic acid and glycerol. The uptake of chromium by S. polyrrhiza followed Michaelis-Menten kinetics of active ion uptake. Interaction between Cr and ascorbic acid, oxalic acid, and lactic acid decreased Cr uptake, whereas citric acid, glycerol, and tartaric acid increased it. Supplementation of LMWOCs to Cr(VI) containing media decreased the MDA content of the plants. Multiple regression models revealed that LMWOCs decrease lipid peroxidation independently, as well as that induced by Cr(VI). It was found that superoxide dismutase (SOD), guaiacol peroxidase (GPX), and catalase (CAT) activities were increased significantly in plants growing in media containing Cr(VI). The study established that lactic acid, citric acid, ascorbic acid, and glycerol were most effective in increasing the Cr(VI) phytoremediating potential of S. polyrrhiza and LMWOCs with reducing or chelating properties decrease Cr(VI) stress in S. polyrrhiza.
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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) | 0.000 | 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".