Development and characterization of EST-SSR markers for the crown rust pathogen of ryegrass (<i>Puccinia coronata</i>f.sp.<i>lolii</i>)
Bibliographic record
Abstract
The causative organism of crown rust in ryegrasses (Puccinia coronata f.sp. lolii) is an obligate biotroph that causes significant economic losses within the temperate grazing industries of dairy, meat, and wool production. This study reports on the development, transferability, and utility of gene-associated simple sequence repeat (SSR) molecular markers for crown rust. Analysis of 1,100 expressed sequence tag (EST) sequences from a urediniospore-derived cDNA library detected 55 SSR loci. The majority of EST-SSR arrays contained perfect trinucleotide repeats with consistently low repeat numbers, and the motifs (ACC)n and (CAT)n were most commonly represented. DNA extraction from single pustules, in conjunction with multiple displacement amplification, provided the basis for PCR-based screening to evaluate genetic marker performance. An example of the identification of intraspecific genetic diversity was obtained from the analysis of 16 P. coronata isolates originating from the United Kingdom, Australia, New Zealand, and Japan. A subset of 12 robust EST-SSR markers was informative for determination of pathogen diversity within and between these localities. It was also demonstrated that crown rust EST-SSR markers were capable of cross-amplification in closely related fungal taxa (Puccinia spp.) and filamentous fungi within the Ascomycota.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".