Genetic diversity and GWAS of agronomic traits using an ICARDA lentil (<i>Lens culinaris</i> Medik.) Reference Plus collection
Bibliographic record
Abstract
Abstract Genotyping of lentil plant genetic resources holds the promise to increase the identification and utilization of useful genetic diversity for crop improvement. The International Center for Agriculture Research in the Dry Areas (ICARDA) lentil reference set plus collection of 176 accessions was genotyped using genotyping-by-sequencing (GBS) and 22,555 SNPs were identified. The population structure was investigated using Bayesian analysis (STRUCTURE, k = 3) and principal component analysis. The two methods are in concordance. Genome-wide association analysis (GWAS) using the filtered SNP set and ICARDA historical phenotypic data discovered putative markers for several agronomic traits including days to first flower, seeds per pod, seed weight and days to maturity. The genetic and genomic resources developed and utilized in this study are available to the research community interested in exploring the ICARDA reference set plus collection using GWAS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".