Bioengineering nitrogen acquisition in rice: can novel initiatives in rice genomics and physiology contribute to global food security?
Bibliographic record
Abstract
Rice is the most important crop species on earth, providing staple food for 70% of the world's human population. Over the past four decades, successes in classical breeding, fertilization, pest control, irrigation and expansion of arable land have massively increased global rice production, enabling crop scientists and farmers to stave off anticipated famines. If current projections for human population growth are correct, however, present rice yields will be insufficient within a few years. Rice yields will have to increase by an estimated 60% in the next 30 years, or global food security will be in danger. The classical methods of previous green revolutions alone will probably not be able to meet this challenge, without being coupled to recombinant DNA technology. Here, we focus on the promise of these modern technologies in the area of nitrogen acquisition in rice, recognizing that nitrogen deficiency compromises the realization of rice yield potential in the field more than any other single factor. We summarize rice-specific advances in four key areas of research: (1). nitrogen fixation, (2). primary nitrogen acquisition, (3). manipulations of internal nitrogen metabolism, and (4). interactions between nitrogen and photosynthesis. We develop a model for future plant breeding possibilities, pointing out the importance of coming to terms with the complex interactions among the physiological components under manipulation, in the context of ensuring proper targeting of intellectual and financial resources in this crucial area of research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".