Exploiting intra-cultivar variation to select for <i>Barley yellow dwarf virus-PAV</i> (BYDV-PAV) resistance in barley
Bibliographic record
Abstract
Selection within elite barley cultivars is assumed to be ineffective due to the belief that inbred cultivars are highly homogeneous. The objective of the present work was to evaluate a selection procedure (Honeycomb design) applied within five barley cultivars (Manel, Rihane, Kounouz, Lemsi, and Imen) and two Tunisian landraces (Ardhaoui and Djebali) under ultra-low plant density (1.2 plants m−2) towards selecting high-performance lines with resistance to Barley yellow dwarf virus-PAV (BYDV-PAV). Lines selected through this process were further field-evaluated in hill plots under artificial BYDV-PAV inoculation and uninoculated control conditions during the 2016–2017 cropping season. Artificial inoculation in the field caused a severe reduction in agronomic performance traits, with yield loss reaching around 60%. However, two lines (IH16-H1 and IH4-L0) originating from cultivar Imen were significantly superior over the mother variety in the control field, showing at the same time minimum yield loss after BYDV-PAV inoculation not exceeding 10%, similar to the resistant check. Genotyping of the lines for the Ryd2 and Ryd3 resistance genes and assessment of visual symptoms in the field associated with reduction in yield revealed an additive effect of the genes conferring resistance to BYDV-PAV. However, there were lines with genotypic patterns that did not match the patterns of the source material, providing insights for exploitable intra-cultivar diversity within the barley cultivars and landraces assessed.
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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 | 0.000 | 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 | 0.001 |
| 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".