An advanced lentil backcross population developed from a cross between <i>Lens culinaris</i> × <i>L. ervoides</i> for future disease resistance and genomic studies
Bibliographic record
Abstract
Abstract Genetically accessible variation to some of the abiotic and biotic stresses are limited in the cultivated lentil ( Lens culinaris Medik.) germplasm. Introgression of novel alleles from its wild relative species will be useful for enhancing the genetic improvement of the crop. L. ervoides , one of the wild relatives of lentil, is a proven source of disease resistance for the crop. Here we introduce a lentil advanced backcross (LABC-01) population developed in cultivar ‘CDC Redberry’ background, based on L. ervoides alleles derived from an interspecific recombinant inbred population, LR-59-81. Two-hundred and seventeen individuals of the LABC-01 population at BC 2 F 3:4 generation were screened for the race 0 of anthracnose ( Colletotrichum lentis ) and stemphylium blight ( Stemphylium botryosum ) under controlled conditions. The population showed significant variations for both diseases and the transfer of resistance alleles into the elite cultivar was evident. It also segregated for other traits such as days to flowering, seed coat colour, seed coat pattern and flower colour. Overall, we showed that LABC-01 population can be used in breeding programmes worldwide to improve disease resistance and will be available as a valuable genetic resource for future genetic analysis of desired loci introgressed from L. ervoides .
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".