Capturing variation in <i>Lens</i> (Fabaceae): Development and utility of an exome capture array for lentil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Premise of the Study Lentil is an important legume crop with reduced genetic diversity caused by domestication bottlenecks. Due to its large and complex genome, tools for reduced representation sequencing are needed. We developed an exome capture array for use in various genetic diversity studies. Methods Based on the CDC Redberry draft genome, we developed an exome capture array using multiple sources of transcript resources. The probes were designed to target not only the cultivated lentil, but also wild species. We assessed the utility of the developed method by applying the generated data set to population structure and phylogenetic analyses. Results The data set includes 16 wild lentils and 22 cultivar accessions of lentil. Alignment rates were over 90%, and the genic regions were well represented in the capture array. After stringent filtering, 6.5 million high‐quality variants were called, and the data set was used to assess the interspecific relationships within the genus Lens . Discussion The developed exome capture array provides large amounts of genomic data to be used in many downstream analyses. The method will have useful applications in marker‐assisted breeding programs aiming to improve the quality of cultivated lentil.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it