Removal of Cyanide-Contaminated Water by Vetiver Grasses
Bibliographic record
Abstract
Vetiver grass and it usages have been widely investigated in many researches as the preferred plant species due to its known efficiency, low cost, the ease of availability and spread. This research aimed to use four different vetiver grass (Vetiveria zizanioides) ecotypes to remove cyanide (CN-)-contaminated water for improve its quality. Growth capability, tolerance and removal efficiency were evaluated. The results showed that the vetiver grass had a 100% survival rate for one month after planting. Songkhlar3 had the longest leaves, followed by Surat-Thani, Sri Lanka and Monto, respectively. Root lengths of all ecotypes showed no significant differences (p ≤ 0.05). All vetiver grass ecotypes could potentially purify CN--contaminated water at lower concentrations of ≤ 35 mg CN-/L. The Monto ecotype had the highest CN- removal efficiency at all CN- concentration levels, showing 100% CN- removal from the 5-45 mg CN-/L contaminated water samples within 2-5 weeks growth. The tolerance of vetiver grass to CN- was a more important factor than growth rate when selecting a vetiver grass ecotype for CN- phytoremediation.
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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".