Intensive and extensive sampling techniques used to measure genetic diversity of <i>Ustilago tritici</i> , using virulence and DNA polymorphism
Bibliographic record
Abstract
Assessment of genetic variability within a natural population of a pathogen is dependent on obtaining a genetically diverse collection of isolates for analysis. Collections of isolates of Ustilago tritici are traditionally made by sampling one smutted head of wheat per field from a large number of fields over a large area. However, there is little evidence that extensively sampling one smutted head from each of many fields over a large area yields a more genetically diverse collection of isolates than intensively sampling many smutted heads from each of a few fields. The objective of this study was to compare genetic diversity derived from intensive sampling of U. tritici (80 isolates from 4 fields) with that derived from extensive sampling (81 isolates from 81 fields). Genetic diversity was measured using virulence data and amplified fragment length polymorphism (AFLP). The virulence of the isolates was assessed on five differential hosts, and the AFLP data were obtained using 10 selective primers that yielded 23 polymorphic bands. Genetic diversity was similar within the extensive- and the intensive-sampling collections, when measured using virulence data, but it was more variable within the intensive-sampling collection than within the extensive-sampling collection, when measured using AFLP. In general, the results of AFLP analysis indicated a higher degree of genetic variability than did the virulence data. The results also indicated that host genotypes can strongly influence the genetic variability within populations of an obligate pathogen like U. tritici when this variability is measured on virulence data, but that host genotype has a weaker influence on diversity measured by AFLP.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".