A Comprehensive Review on Role Of Micronutrients In Legumes
Bibliographic record
Abstract
This review article delves into the pivotal role of micronutrients, including iron, zinc, and manganese, in legumes, elucidating their impact on plant growth, development, and overall nutritional quality. Micronutrient deficiencies in legumes can lead to detrimental consequences such as stunted growth, reduced yield, and compromised plant health. Moreover, the nutritional benefits of legumes for both human and animal consumption are underscored, emphasizing their significance in providing essential nutrients like proteins, carbohydrates, and dietary fibers. Furthermore, the review explores how legumes contribute to sustainable agriculture practices through nitrogen fixation, symbiotic relationships with bacteria, and soil health improvement. By understanding and managing the micronutrient requirements of legumes, we can enhance their symbiotic nitrogen fixation capacity, improve soil fertility, and reduce dependency on synthetic fertilizers. In conclusion, this review underscores the importance of addressing micronutrient deficiencies in legumes to optimize crop productivity, ensure food security, and promote sustainable agricultural practices. By integrating effective management strategies and biofortification techniques, we can harness the nutritional potential of legumes to support global health and well-being while fostering environmental sustainability.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".