Intelligent Characterization of Lentil Genetic Resources: Evolutionary History, Genetic Diversity of Germplasm, and the Need for Well‐Represented Collections
Bibliographic record
Abstract
The genetic and phenotypic characterization of crops allows us to elucidate their evolutionary and domestication history, the genetic basis of important traits, and the use of variation present in landraces and wild relatives to enhance resilience. In this context, we aim to provide an overview of the main genetic resources developed for lentil and their main outcomes, and to suggest protocols for continued work on this important crop. Lens culinaris is the third-most-important cool-season grain and its use is increasing as a quick-cooking, nutritious, plant-based source of protein. L. culinaris was domesticated in the Fertile Crescent, and six additional wild taxa (L. orientalis, L. tomentosus, L. odemensis, L. lamottei, L. ervoides, and L. nigricans) are recognized. Numerous genetic diversity studies have shown that wild relatives present high levels of genetic variation and provide a reservoir of alleles that can be used for breeding programs. Furthermore, the integration of genetics/genomics and breeding techniques has resulted in identification of quantitative trait loci and genes related to attributes of interest. Genetic maps, massive genotyping, marker-assisted selection, and genomic selection are some of the genetic resources generated and applied in lentil. In addition, despite its size (∼4 Gbp) and complexity, the L. culinaris genome has been assembled, allowing a deeper understanding of its architecture. Still, major knowledge gaps exist in lentil, and a deeper understanding and characterization of germplasm resources, including wild relatives, is critical to lentil breeding and improvement. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Recording of lentil seed descriptors Basic Protocol 2: Lentil seed imaging Basic Protocol 3: Lentil seed increase Basic Protocol 4: Recording of primary lentil seed INCREASE descriptors.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".