Efficacy of drip irrigation in controlling heavy-metal accumulation in soil and crop
Bibliographic record
Abstract
This study was aimed at identifying how drip irrigation could be useful in controlling heavy-metal issues, practically and affordably. A vegetable crop (i.e. cauliflower) was the subject of the test. Heavy-metal accumulation in soils and uptake by cauliflower curds were observed for two consecutive years. Municipal wastewater and groundwater were used for irrigation, to make it a comparative study. There were eight treatments: drip irrigation with groundwater through inline (non-pressure-compensating) surface drip (T1), inline subsurface drip (T2), bioline (pressure-compensating) subsurface drip (T3), bioline surface drip (T4) and the same drip systems using primarily treated municipal wastewater (T5to T8). The results showed that significantly higher concentrations of heavy metals – namely, copper, iron, manganese and zinc – were recorded in cauliflower curds irrigated with wastewater compared with those irrigated with groundwater. Subsurface placement of pressure-compensating drip laterals was found more effective in reducing the heavy-metal concentrations in both cauliflower and soil profile compared with surface-placed non-pressure-compensating drip laterals. This study suggests that drip irrigation systems could be an effective method to reduce heavy-metal concentration in vegetable crops and soils irrigated with treated municipal wastewater.
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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 | 0.001 |
| 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".