The relationship between molecular and pathogenic variability in a Saskatchewan population of<i>Mycosphaerella graminicola</i>
Bibliographic record
Abstract
Considerable molecular variability exists in Mycosphaerella graminicola, the cause of the important foliar disease septoria tritici blotch of wheat, but the relationship of this variability to pathogenic variability is unknown. This study examined this relationship to determine whether estimation of molecular variability has value for predicting the potential for pathogenic change in this pathogen. Ninety isolates of M. graminicola were collected using hierarchical sampling of leaves and lesions from 10 locations within a field, near Saskatoon, Saskatchewan, seeded to the hexaploid wheat cultivar ‘CDC Teal’. The pathogenicity of these isolates was evaluated on the susceptible cultivar ‘Conway’ by measuring incubation period, latent period, and disease severity. Significant differences were found among isolates for all components at the lesion sampling level but not at the leaf and location level. The percentage of polymorphic loci and gene diversity of the population was estimated using 15 RAPD primers. A high level of molecular variability existed within the population (H T = 0.179). Partitioning this variability showed that 64% of the variability was distributed within leaves, 22% among leaves, and only 14% among locations. A poor relationship between molecular and pathogenic variability was found, suggesting that DNA fingerprinting has little value for monitoring the development of new virulent genotypes of the pathogen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".