Diverse traits for pathogen fitness in<i>Gibberella zeae</i>
Bibliographic record
Abstract
Gibberella zeae is an important pathogen of wheat, maize, and other cereal crops worldwide. Pathogen fitness in G. zeae is the outcome of selection for traits that increase its ability to survive and reproduce in plant pathosystems. Current research on mechanisms of pathogen fitness uses tools such as production of specific mutations by targeted gene disruption and analysis of genetic variation in natural populations. Gene disruption experiments indicate that production of the trichothecene deoxynivalenol (DON) enhances virulence on wheat and maize, and that production of sexual spores enhances head blight on wheat under field conditions. Natural populations from the U.S.A. and from Nepal differ significantly in virulence on wheat, sexual fertility, and trichothecene chemotype. Strains from both populations can produce DON, but only strains from Nepal can also produce nivalenol, which differs from DON by the addition of a hydroxyl group. Genetic analyses are underway to investigate associations of pathogen fitness of G. zeae with strain genotype, trichothecene chemotype, and other traits.Key words: Fusarium graminearum, wheat, head blight, trichothecenes, mating-type genes, population genetics.
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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 | 0.000 |
| 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) | 0.001 | 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".