Inoculum threshold for stripe rust infection in wheat
Bibliographic record
Abstract
Stripe rust (Puccinia striiformis f. sp. tritici (Pst)) and powdery mildew (Blumeria graminis f. sp. tritici (Bgt)) are important diseases of wheat in Canada and worldwide. Molecular detection methods permit spore detection of few spores; therefore, there is a need to determine initial inoculum thresholds for pathogens to cause disease under both controlled environments and in the field. Susceptible wheat cultivars ‘Avocet’ and ‘AC Barrie’ were inoculated with different quantities of spores (0, 103, 104, 105, 106, and 107) of Pst and Bgt. Disease incidence, severity and infection type were evaluated. Results of controlled environment studies showed that the minimum number of spores necessary to cause appreciable incidence and severity for Pst was at higher spore concentrations of 105–106 spores. Conversely, low incidence and severity levels were observed at 103–104 spores for Bgt. Despite occurrence of natural Pst infection, results of field studies in 2016 and 2017 in Southern Alberta demonstrated that significant increases in severity levels were observed following application of 1.2 × 107 spores. Collectively, these results demonstrated that stripe rust severities increased with increasing spore concentration only at high spore levels. In contrast, Bgt severity increased with spore concentration from 103 to 107 spores mL−1. In vitro and in vivo spore germination tests demonstrated germination rates of Pst spores were reduced at lower spore concentrations compared to germination rates at the higher concentrations. Understanding of minimum spore numbers required for disease development will be a prerequisite for predicting epidemics and devising fungicide control measures for future sustainable agricultural systems.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".