Genetic architecture of anthocyanin pigment traits and purple spot ( <i>Stemphylium vesicarium</i> ) resistance in an F <sub>1</sub> pseudo‐testcross population of asparagus
Bibliographic record
Abstract
Abstract Stemphylium vesicarium (Wallr.) Simmons is a plant pathogenic fungus causing purple spot in both fern and spears of asparagus ( Asparagus officinalis L.). Although the fern can be sprayed with fungicides to control the disease, pesticide applications during spear harvest are restricted. Infected spears can develop prominent pigmentation at lesion sites, reducing marketable yield. Breeding resistant asparagus cultivars with decreased lesion numbers and reduced purpling at the site of infection is considered the most economical and sustainable approach to combat this disease. The objectives of this study were to determine the genetic architectures of, and relationships among, anthocyanin pigment expression in spear scale leaves (ALS) and spear lesions (APS) and purple spot levels in spears (NPS) and fern (PSF). Traits were phenotyped over 2 years under natural conditions in an F 1 pseudo‐testcross population, and quantitative trait loci (QTL) were mapped. ALS, APS, NPS, and PSF were not correlated, suggesting independent regulation of the anthocyanin pathway in scale leaves and lesions and no relationship between pigment and disease. Segregation, 3 red:1 purple and 3 red:13 purple, was observed in scale leaves and lesions, respectively. Two stable QTL for each of ASL, APS, and NPS, one tentative QTL for ASL, four tentative QTL for APS, two tentative QTL for NPS, and three tentative QTL for PSF were identified. Candidate genes were found for four loci. This study advances the genetic understanding of anthocyanin pigmentation at a tissue‐specific level, and purple spot disease severity in spears and fern, supporting future breeding efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".