Impact of brewery sludge application on heavy metal build-up, translocation, growth and yield of bread wheat (Triticum aestivum L.) crop in Northern Ethiopia
Bibliographic record
Abstract
In a field study, the impact of different levels of brewery sludge (BS) enrichment on Triticum aestivum L . (wheat plants) was examined in terms of growth, yield, heavy metal absorption, and potential health risks linked to plant consumption. Using a randomized complete block design with seven treatments and three blocks, the study showed that applying up to 12 t ha -1 brewery sludge significantly improved all agronomic parameters (except harvest index) compared to control and mineral-fertilized soil. Heavy metal translocation was generally low, except for Cu and Pb. The sequence of heavy metal translocation was Cu > Pb > Cd > Ni > Zn > Mn > Cr from soil to spikes and Cu > Zn > Mn > Pb > Ni > Cd > Cr from soil to grain. Heavy metal loads were mostly higher in roots than in the above-ground crop parts. The target hazard quotient (THQ), hazard index (HI), and target cancer risk (TCR) within wheat grain remained within safe limits for all BS treatments. Consequently, consuming this wheat grain is considered safe regarding heavy metals. Thus, utilizing brewery sludge at 12 t ha -1 as a fertilizer for wheat production and as an alternative method for sludge disposal is plausible.
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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".