Leaf cell membrane stability‐based mechanisms of zinc nutrition in mitigating salinity stress in rice
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Bibliographic record
Abstract
Abstract Excess salt affects about 955 million ha of arable land worldwide, and 49% of agricultural land is Zn‐deficient. Soil salinity and zinc deficiency can intensify plant abiotic stress. The mechanisms by which Zn can mitigate salinity effects on plant functions are not well understood. We conducted an experiment to determine how Zn and salinity effects on rice plant retention of Zn, K + and the salt ion Na + affect chlorophyll formation, leaf cell membrane stability and grain yield. We examined the mechanisms of Zn nutrition in mitigating salinity stress by examining plant physiology and nutrition. We used native Zn‐deficient soils (control), four salinity ( EC ) and Zn treatments – Zn 10 mg·kg −1 (Zn 10 ), EC 5 dS ·m −1 ( EC 5 ), Zn 10 + EC 5 and Zn 15 + EC 5 , a coarse rice ( KS ‐282) and a fine rice (Basmati‐515) in the study. Our results showed that Zn alone (Zn 10 ) significantly increased rice tolerance to salinity stress by promoting Zn/K + retention, inhibiting plant Na + uptake and enhancing leaf cell membrane stability and chlorophyll formation in both rice cultivars in native alkaline, Zn‐deficient soils ( P < 0.05). Further, under the salinity treatment ( EC 5 ), Zn inputs (10–15 mg·kg −1 ) could also significantly promote rice plant Zn/K + retention and reduce plant Na + uptake, and thus increased leaf cell membrane stability and grain yield. Coarse rice was more salinity‐tolerant than fine rice, having significantly higher Zn/K + nutrient retention. The mechanistic basis of Zn nutrition in mitigating salinity impacts was through promoting plant Zn/K + uptake and inhibiting plant Na + uptake, which could result in increased plant physiological vigour, leaf cell membrane stability and rice productivity.
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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 it