Barley Straw Biochar and Compost Affect Heavy Metal Transport in Soil and Uptake by Potatoes Grown under Wastewater Irrigation
Bibliographic record
Abstract
Wastewater can supplement freshwater in agriculture; however, it contains toxic heavy metals such as cadmium, chromium, and lead that are hazardous to humans and the environment. We investigated the effects of barley straw biochar, green and table waste compost, and their mix on heavy metal transport in soil and uptake by potatoes (Solanum tuberosum L.) irrigated with synthetic wastewater for two years. In both years, amending soil with compost significantly reduced (p ≤ 0.05) cadmium uptake in potato flesh, skin, roots, and stems; zinc uptake in potato skin and roots; and copper uptake in potato flesh due to increased soil cation-exchange capacity, dissolved organic carbon, and soil pH. Co-amending the soil with compost and 3% biochar significantly reduced (p ≤ 0.05) the bioavailability of cadmium, copper, and zinc in the contaminated soil. Relative to the non-amended soils, soil amendment with biochar, compost, and their mix affected neither the transport of chromium, iron, and lead in the soils nor their uptake by potatoes. It was concluded that amending soil with barley straw biochar and/or compost produced from city green table waste could be used to improve the safety of wastewater irrigated potatoes, depending on the biochar application rate and heavy metal type.
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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".