Advancements in Lentil Genomics for Enhanced Crop Breeding: A Review
Bibliographic record
Abstract
Lentil (Lens culinaris Medik) is an essential pulse crop that is widely grown for its high nutritional value, notably its high protein content, making it an important dietary component for vegetarians and vegans. Despite being the world's fifth most produced pulse, with large contributions from Canada and India, lentil production confronts obstacles such as poor productivity due to limited genetic improvement against biotic and abiotic stresses under rainfed cultivation conditions. Recent advances in lentil genetics and genomics, such as the discovery of genes related to yield, disease resistance, and nutritional content, have boosted breeding efforts to generate improved lentil varieties. The use of contemporary genomic techniques like molecular markers, marker-assisted selection (MAS), genomic selection (GS), and next-generation sequencing (NGS) technology has sped up the discovery of quantitative trait loci (QTLs) and the production of novel cultivars with superior agronomic characteristics. Databases such as NCBI and ENA, as well as specialized resources like KnowPulse, provide critical genomic data, while the creation of lentil genome assemblies, notably the CDC Redberry variety, has improved our understanding of lentil genetics. These resources help to solve the constraints of traditional breeding, particularly for complex characteristics impacted by genotype-environment interactions, opening the way for more robust and productive lentil varieties. Although the application of advanced tools such as genetic engineering, cisgenesis, and genome editing has moved more slowly in lentils than in other crops, their potential to improve lentil output is encouraging. Recent studies on lentil genomes, together with the creation of increased genetic resources and cutting-edge techniques, offer the ability to overcome production constraints and dramatically increase lentil production and quality throughout the world.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".