Genomic resources for improving food legume crops
Bibliographic record
Abstract
SUMMARY Food legumes are the main source of dietary protein for a large part of the world's population, and also play an important role in maintaining soil fertility through nitrogen fixation. However, legume yields and production are often limited by large genotype×environment (G×E) interactions that influence the expression of agronomically important, complex quantitative traits. Consequently, genetic improvement has been slower than expected. Molecular marker technology enables genetic dissection of such complex traits, allowing breeders to identify genomic regions on the chromosome that have main effects or interactive effects. A number of genomic resources have been developed in several legume species during the last two decades, and provide a platform for exploiting marker technology. The present paper reviews the available genomic resources in food legumes: linkage maps, high-throughput sequencing technologies, expression sequence tag (EST) databases, genome sequences, DNA chips, targeting induced local lesions in genomes (TILLING), bacterial artificial chromosome (BAC) libraries and others. It also describes how these resources are being used to tag and map genes/quantitative trait loci (QTLs) for domesticated and other agronomically important traits. This information is important to genetic improvement efforts aiming at improving food and nutrition security worldwide.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".