Bibliographic record
Abstract
The use of chemical fertilizers in agriculture has been a prime factor in the consistent increase in crop yields and has supported the escalation of the world’s human population over the past 100 years. However, it has been recognized only recentlythat the use of artificial fertilizers presents enormous challenges as a consequence of environmental pollution and greenhouse gas emissions.The combination of increasing fertilizer prices, their poor efficiency of use in combination with concerns over their impacts on the environment, all point to the need to change the way agriculture is conducted and to provide safe and environmentally sustainable solutions to the problem of nitrogen limitation in crop plants. This chapter focuses on one potential solution to this issue, i.e., the engineering of biological nitrogen fixation, to supply nitrogen to cereal crops. Three different approaches to this goal are considered. Firstly, the development of synthetic symbioses between cereals and nitrogen-fixing bacteria in the rhizosphere in which ammonia excreted by the bacteria can be exchanged for carbon provided by the plant. Secondly, more sophisticated approach would be to engineer cereals with the capacity to form root nodules, thus providing an ideal environment for symbiosis with the nitrogen-fixing bacteria. Thirdly, transforming plants with the genes required to express a functional nitrogenase enzyme replete with its cofactors and therefore endowing crops with the ability to directly fix atmospheric nitrogen into ammonia. Challenges to the successful fulfilment of each of these three approaches are discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".