Definition of sustainable and unsustainable issues in nutrient management of modern agriculture
Bibliographic record
Abstract
Abstract. Sustainable management of nutrients in agricultural systems is critical for sufficient production of nutritious foods and to minimize environmental pollution. In this overview, we discuss some of the most important factors influencing nutrient cycling, and how practices for sustainable nutrient management can be optimized. In most cases, problems are associated with excessive use of nutrients (manures, other organic amendments, and inorganic fertilizers). Options for dealing with such problems at the farm level include: reducing nutrient inputs to balance exports, increasing the land area on which manures are applied, and export of excess nutrients from the farm in the form of value‐added products. These strategies can be used singly, or in combination. Nutrients in the human food chain are often not recycled back to primary crop production. To manage such issues, and avoid regional nutrient accumulations, we need to develop a better understanding of large‐scale nutrient flows, and develop policies to manage them. We stress the importance of scale when considering nutrient management in the future.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".