Rapid generation cycling of an F<sub>2</sub> population derived from a cross between <i>Lens culinaris</i> Medik. and <i>Lens ervoides</i> (Brign.) Grande after aphanomyces root rot selection
Bibliographic record
Abstract
Abstract Cultivated lentil (Lens culinaris Medik.) is susceptible to aphanomyces root rot (ARR), whereas partial resistance is present in wild lentil including Lens ervoides (Brign.) Grande. Approximately six generations of selfing are required to fix a desired trait in a population, which usually requires 2 years in a breeding programme, so the primary objective was to develop a rapid generation cycling (RGC) technique that achieves this goal in 1 year. Rapid generation cycling was then tested on an F2 population (LR‐59) derived from a L. culinaris × L. ervoides cross in combination with a reliable ARR screening technique, which generates a wide range of disease severities conducive to selection. Phenotyping of an F2 population of more than 1,200 plants resulted in scores ranging from 2.4 to 4.0 on a scale from zero to five. Plants with scores lower than 4.0 were selected for advancement for five generations using a modified single‐seed descent method, optimum growing conditions, 20‐hr photoperiod and harvest of immature seeds. Seeds were germinated in a 100 μM gibberellin solution. Average generation length after phenotyping was 56 days resulting in five generations within approximately 300 days. Using a modified inoculation protocol, ARR phenotyping of the F7 population resulted in scores ranging from 1.4 to 4.0. This inexpensive, nonsterile speed breeding protocol saves 1 year in the development of lentil varieties with improved ARR resistance.
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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 | 0.000 |
| 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 | 0.001 | 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".