Zinc bioavailability response curvature in wheat grains under incremental zinc applications
Bibliographic record
Abstract
Zinc application is generally recommended to enrich wheat grains with Zn; however, its influence on Zn bioavailability to humans has not received appreciable attention from scientists. In this pot experiment, seven Zn rates (from 0 to 18 mg kg−1 soil) were applied to two wheat cultivars (Shafaq-2006 and Auqab-2000). Application of Zn significantly increased grain yield, grain Zn concentration and estimated Zn bioavailability, and significantly decreased grain phytate concentration and [phytate]:[Zn] ratio in wheat grains. The response of grain yield to Zn application was quadratic, whereas maximum grain yield was estimated to be achieved at 10.8 mg Zn kg−1 soil for Shafaq-2006 and 7.4 mg Zn kg−1 soil for Auqab-2000. These estimated Zn rates were suitable for increasing grain Zn concentration and Zn bioavailability (>2.9 mg Zn in 300 g grains) to optimum levels required for better human nutrition. Conclusively, Zn fertilization for Zn biofortification may be practiced on the bases of response curve studies aimed at maximizing grain yield and optimum Zn bioavailability. Moreover, additive Zn application progressively reduced the grain Fe concentration and increased the grain [phytate]:[Fe] ratio. However, a medium Zn application rate increased grain Ca concentration and decreased the grain [phytate]:[Ca] ratio. Hence, rate of Zn application for mineral biofortification needs to be carefully selected.
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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 | 0.001 |
| 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".