Engineering nitrogen use efficient crop plants: the current status
Bibliographic record
Abstract
In the last 40 years the amount of synthetic nitrogen (N) applied to crops has risen drastically, resulting in significant increases in yield but with considerable impacts on the environment. A requirement for crops that require decreased N fertilizer levels has been recognized in the call for a 'Second Green Revolution' and research in the field of nitrogen use efficiency (NUE) has continued to grow. This has prompted a search to identify genes that improve the NUE of crop plants, with candidate NUE genes existing in pathways relating to N uptake, assimilation, amino acid biosynthesis, C/N storage and metabolism, signalling and regulation of N metabolism and translocation, remobilization and senescence. Herein is a review of the approaches taken to determine possible NUE candidate genes, an overview of experimental study of these genes as effectors of NUE in both cereal and non-cereal plants and the processes of commercialization of enhanced NUE crop plants. Patents issued regarding increased NUE in plants as well as gene pyramiding studies are also discussed as well as future directions of NUE research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".