Hormonal regulation of reproductive growth under normal and heat-stress conditions in legume and other model crop species
Bibliographic record
Abstract
Legume crops are grown throughout the world and provide an excellent food source of digestible protein and starch, as well as dietary fibre, vitamins, minerals, and flavonoids. Fruit and seeds from legumes are also an important source of vegetables for a well-balanced diet. A trend in elevated temperature as a result of climate change increases the risk of a heat stress-induced reduction in legume crop yield. High temperatures during the crop reproductive development phase are particularly detrimental to fruit/seed production because the growth and development of the reproductive tissues are sensitive to small changes in temperature. Hormones are signalling molecules that play important roles in a plant's ability to integrate different environmental inputs and modify their developmental processes to optimize growth, survival, and reproduction. This review focuses on the hormonal regulation of reproductive development and heat stress-induced alteration of this regulation during (i) pollination, (ii) early fruit set, and (iii) seed development that affects fruit/seed yield in legume and other model crops. Further understanding of hormone-regulated reproductive growth under non-stress and heat-stress conditions can aid in trait selection and the development of gene modification strategies and cultural practices to improve heat tolerance in legume crops contributing to improved food security.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".