Legume Production and Climate Change
Bibliographic record
Abstract
Legumes are grown in more than 120 countries across all the continents. Ninety per cent of the global production of legumes is in a small group of countries such as India, Turkey, Pakistan, Bangladesh, Nepal, Iran, Mexico, Myanmar, Ethiopia, Australia, Spain, Canada, Syria, Morocco, and Egypt. Legumes are vegetarian sources of proteins and are an integral part of daily diet in several forms universally. Legume crops are great worth crops and play a dynamic role in crop diversification and economic sustainability of legume farming systems in dry areas. Most of the legume crops are grown under rainfed agroecosystem with poor level of input use which has resulted in low production. In this chapter we focus on (i) ecology and adaptation of legumes crop, (ii) physiological responses of grain legumes to stress environments, (iii) agronomic approaches to stress management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".