A Detached Spear Screen for Purple Spot ( <i>Stemphylium vesicarium</i> ) on Asparagus Spears that Correlates with Natural Field Infection
Bibliographic record
Abstract
An effective screening method for purple spot disease, caused by Stemphylium vesicarium, in asparagus spears is crucial for breeding resistant cultivars. Field evaluations from natural infection are often inconsistent because of environmental variability, and assays using inoculated cut spears have shown unreliable results. The objective of this research was to develop a growth chamber screening method that correlates with field disease severity and can be readily adopted by breeding programs. Intact spears of four asparagus cultivars, Gijnlim (G.J.), Guelph Millennium (G.M.), Guelph Eclipse (G.E.), and Jersey Giant (J.G.), were evaluated for disease severity during natural field infections. Spears were also harvested when field infections were low, wounded, inoculated with conidia of S. vesicarium, placed standing in solutions of 0, 5, or 10% sucrose, incubated for 7 days under high humidity, and evaluated. Endogenous carbohydrates were also measured in spears without disease for separate field and growth chamber experiments. Under natural infections, G.J. and G.M. had lower disease severity and higher carbohydrate concentrations compared with G.E. and J.G. For cut, inoculated spears in the growth chamber, disease severity correlated with field infections only when spears were incubated with bases standing in 5 or 10% sucrose. Resistant cultivars showed stable carbohydrate levels, whereas susceptible cultivars exhibited carbohydrate accumulation that was associated with increased disease severity. The growth chamber assay provides a reliable and practical screening method for resistance to purple spot, offering a valuable tool for breeding improved disease resistance in asparagus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".