Wild Lathyrus—A Treasure of Novel Diversity
Bibliographic record
Abstract
Grasspea (Lathyrus sativus L.) is a climate-smart legume crop with adaptation to fragile agroecosystems. The genus Lathyrus is recognized for its vast genetic diversity, encompassing over 160 species, many of which are cultivated for various purposes across different regions of the world. Among these, Lathyrus sativus is widely cultivated as food, feed, and fodder in South Asia, Sub-Saharan Africa, and the Central and West Asia and North Africa (CWANA) regions. Its global cultivation has declined substantially due to the stigma posed by the presence of neurotoxin β-N-oxalyl-L-α, β-diaminopropionic acid (β-ODAP) in its seeds and green foliage. Overconsumption for a longer period of grasspea seeds harvested from landraces may lead to a neurological disorder called neurolathyrism in humans. ODAP is an obstacle for grasspea expansion, but crop wild relatives (CWRs) have been found to offer a solution. The incorporation of CWRs, particularly Lathyrus cicera, and landraces into breeding programs may reduce the ODAP content in grasspea varieties to a safer level. Recent advances in genomics-assisted breeding have expanded the potential for utilizing challenging CWRs to develop grasspea varieties that combine ultra-low ODAP levels with improved yield, stability, and adaptability. Further progress in omics technologies—such as transcriptomics, proteomics, and metabolomics—along with genome sequencing and editing, has greatly accelerated the development of grasspea varieties with reduced or zero ODAP content, while also enhancing the plant’s agronomic value. This review highlights the significance of utilizing CWRs in pre-breeding programs, and harnessing advanced tools and technologies to enhance the performance, adaptability, and resilience of grasspea in response to changing environmental conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".