Nitrogen use efficiency: re-consideration of the bioengineering approach
Bibliographic record
Abstract
There is considerable confusion about N use efficiency (NUE) in the plant literature. We would like to propose the simple and ubiquitous definitions described by Good et al. (2004) as a starting point for studies of NUE. Based on this terminology, there is evidence from breeding programs for variation in both uptake efficiency (UpE) and utilization efficiency (UtE). Molecular physiology studies typically address mechanisms for improving NUE, but often do not calculate NUE or even acquire appropriate data for calculating NUE. Herein, we report in detail on recent studies involving molecular approaches for improving NUE, and calculate changes in NUE where possible. The evidence suggests that there is potential for improving usage index and UpE in dicots and UpE and UtE in monocots by overexpressing enzymes for N assimilation, specifically glutamine synthetase 1, glutamate synthase, and alanine aminotransferase. If decreased fertilizer-N input and improved NUE are truly goals of the plant biology community, researchers are encouraged to (i) consider the use of both wild type and azygous controls, (ii) compare general NUE (on the basis of grain or biomass yield per unit of applied N) of overexpression mutants and controls at both limiting and non-limiting N levels, (iii) select an appropriate type of specific NUE for assessing the physiological mechanisms involved (uptake versus internal utilization), and (iv) confirm promising results under field conditions.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".