<scp>SRAP</scp> as an Informative Molecular Marker to Study the <i>Fusarium poae</i> Genetic Variability
Bibliographic record
Abstract
Abstract Fusarium poae is one of the Fusarium species isolated from grains associated with Fusarium head blight (FHB), whose occurrence has increased in the last years. In this study, a total of 105 F. poae isolates from Argentina, Belgium, Canada, England, Finland, France, Germany, Hungary, Italy, Luxembourg, Poland, Switzerland and Uruguay were evaluated using sequence‐related amplified polymorphism (SRAP) to analyse the capacity of this molecular marker to evaluate the F. poae genetic variability. The molecular analysis showed high intraspecific variability within F. poae isolates, and a partial relationship was revealed between variability and the host/geographic origin. Analysis of molecular variance (amova) indicated a high genetic variability in the F. poae collection, with most of the genetic variability resulting from differences within, rather than between American and European populations. The analysis of sequenced SRAP fragments targets into hypothetical proteins from different Fusarium species showing that the SRAP technique not only allows studying F. poae genetic variability, but also targets coding regions into the F. poae genome. To our knowledge, this is the first report on genetic variability of F. poae using SRAP technique and also demonstrates the efficacy of this molecular marker to amplify open reading frames in fungus.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".