Phytoextraction of Heavy Metals by Various Vegetable Crops Cultivated on Different Textured Soils Irrigated with City Wastewater
Bibliographic record
Abstract
A challenging task in urban or suburban agriculture is the sustainability of soil health when utilizing city wastewater, or its dilutes, for growing crops. A two-year field experiment was conducted to evaluate the comparative vegetable transfer factors (VTF) for four effluent-irrigated vegetable crops (brinjal, spinach, cauliflower, and lettuce) grown on six study sites (1 acre each), equally divided into two soil textures (sandy loam and clay loam). Comparisons of the VTF factors showed spinach was a significant and the best phytoextractant, having the highest heavy metal values (Zn = 20.2, Cu = 12.3, Fe = 17.1, Mn = 30.3, Cd = 6.1, Cr = 7.6, Ni = 9.2, and Pb = 6.9), followed by cauliflower and brinjal, while lettuce extracted the lowest heavy metal contents (VTF: lettuce: Zn = 8.9, Cu = 4.2, Fe = 9.6, Mn = 6.6, Cd = 4.7, Cr = 2.9, Ni = 5.5, and Pb = 2.5) in response to the main (site and vegetable) or interactive (site * vegetable) effects. We suggest that, while vegetables irrigated with sewage water may extract toxic heavy metals and remediate soil, seriously hazardous/toxic contents in the vegetables may be a significant source of soil and environmental pollution.
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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) | 0.000 | 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 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".