Nitrogen use efficiency (NUE) in rice links to NH4 + toxicity and futile NH4 + cycling in roots
Bibliographic record
Abstract
Rice is known as an ammonium (NH4 +)-tolerant species. Nevertheless, rice can suffer NH4 + toxicity, and excessive use of nitrogen (N) fertilizer has raised NH4 + in many paddy soils to levels that reduce vegetative biomass and yield. Examining whether thresholds of NH4 + toxicity in rice are related to nitrogen-use efficiency (NUE) is the aim of this study. A high-NUE (Wuyunjing 23, W23) and a low-NUE (Guidan 4, GD) rice cultivar were cultivated hydroponically, and growth, root morphology, total N and NH4 + concentration, root oxygen consumption, and transmembrane NH4 + fluxes in the root meristem and elongation zones were determined. We show that W23 possesses greater capacity to resist NH4 + toxicity, while GD is more susceptible. We furthermore show that tissue NH4 + accumulation and futile NH4 + cycling across the root-cell plasma membrane, previously linked to inhibited plant development under elevated NH4 +, are more pronounced in GD. NH4 + efflux in the root elongation zone, measured by SIET, was nearly sevenfold greater in GD than in W23, and this was coupled to strongly stimulated root respiration. In both cultivars, root growth was affected more severely by high NH4 + than shoot growth. High NH4 + mainly inhibited the development of total root length and root area, while the formation of lateral roots was unaffected. It is concluded that the larger degree of seedling growth inhibition in low- vs. high-NUE rice genotypes is associated with significantly enhanced NH4 + cycling and tissue accumulation in the elongation zone of the root.
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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".