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 (T 1 ), inline subsurface drip (T 2 ), bioline (pressure-compensating) subsurface drip (T 3 ), bioline surface drip (T 4 ) and the same drip systems using primarily treated municipal wastewater (T 5 to T 8 ). 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 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 | 0.000 | 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 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".