Fungal Pathogens of Maize Gaining Free Passage Along the Silk Road
Bibliographic record
Abstract
Silks are the long threads at the tips of maize ears onto which pollen land and sperm nuclei travel long distances to fertilize egg cells, giving rise to embryos and seeds; however fungal pathogens also use this route to invade developing grain, causing damaging ear rots with dangerous mycotoxins. This review highlights the importance of silks as the direct highways by which globally important fungal pathogens enter maize kernels. First, the most important silk-entering fungal pathogens in maize are reviewed, including Fusarium graminearum, Fusarium verticillioides, and Aspergillus flavus, and their mycotoxins. Next, we compare the different modes used by each fungal pathogen to invade the silks, including susceptible time intervals and the effects of pollination. Innate silk defences and current strategies to protect silks from ear rot pathogens are reviewed, and future protective strategies and silk-based research are proposed. There is a particular gap in knowledge of how to improve silk health and defences around the time of pollination, and a need for protective silk sprays or other technologies. It is hoped that this review will stimulate innovations in breeding, inputs, and techniques to help growers protect silks, which are expected to become more vulnerable to pathogens due to climate change.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".