Bibliographic record
Abstract
Nitrogen was the most commonly yield-limiting nutrient in all pre-industrial agricultures. Only the Haber-Bosch synthesis of ammonia broke this barrier. The rising dependence on nitrogenous fertilizers, which represents the largest human interference in the biospheric N cycle, has two different roles. In affluent nations it helps to produce excess of food in general, and of animal foods in particular, and it boosts agricultural exports. But for at least a third of humanity in the world's most populous countries the use of N fertilizers makes the difference between malnutrition and adequate diet. Our understanding of human N (protein) needs has undergone many revisions and although some uncertainties still remain it is clear that average protein intakes are excessive in rich countries and inadequate for hundreds of millions of people in Asia, Africa, and Latin America. More dietary protein will be needed to eliminate these disparities but the future global use of N fertilizers can be moderated not just by better agronomic practices but also by higher feeding efficiencies and by gradual changes of prevailing diets. As a result, it could be possible to supply adequate nutrition to the world's growing population without any massive increases of N inputs.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".