Molecular and phenotypic characterization of variation related to pea enation mosaic virus resistance in lentil (<i>Lens culinaris</i>Medik.)
Bibliographic record
Abstract
Jain, S., Porter, L. D., Kumar, A., Mir, R. R., Eigenbrode, S. D. and McPhee, K. E. 2014. Molecular and phenotypic characterization of variation related to pea enation mosaic virus resistance in lentil (Lens culinaris Medik.). Can. J. Plant Sci. 94: 1333–1344. Identification of genetically diverse lentil germplasm with resistance to pea enation mosaic virus (PEMV) through the combined approach of molecular marker analysis and phenotyping could prove useful in breeding programs. A total of 44 lentil (Lens culinaris Medik.) accessions, were screened for resistance to PEMV. Two accessions (PI 431663 and PI 432028) were identified with resistance to PEMV in field tests while several accessions were found resistant in greenhouse screenings. Thirty-six polymorphic simple sequence repeat (SSR) markers which produced 43 loci with 2 to 12 alleles per locus were used for genetic diversity analysis. The polymorphic information content (PIC) values for these markers ranged from 0.22–0.85 with a mean of 0.55 per marker. Using allelic data of 36 SSR primer pairs, dissimilarity ranging from 0.12 to 0.74 was calculated. Cluster analysis performed using the unweighted pair group method with arithmetic mean (UPGMA) determined that most of PEMV-resistant accessions were grouped in one cluster along with other accessions from Iran, Chile, Ethiopia, India, Pakistan, Turkey, Afghanistan and Lebanon. All the adapted cultivars originating from North and South America were grouped in another cluster along with some European accessions. The 44 accessions were classified into 4 subpopulations using Structure 2.2 software complimenting the results of UPGMA analysis and indicated the effect of geographical origin on the grouping of accessions. The results of this study can be used to select genetically diverse PEMV-resistant accessions for lentil improvement programs.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".