Pollination can protect maize ovaries from infection by<i>Ustilago maydis</i>, the corn smut fungus
Bibliographic record
Abstract
Ustilago maydis DC (Corda), the maize smut fungus, causes disease on maize (Zea mays L.) and related species. To determine whether pollination of maize ears affects their susceptibility to U. maydis infection, ears were treated in one of four ways: pollination only, inoculation with compatible haploid U. maydis cells only, pollination followed by inoculation 4 days later, or inoculation followed by pollination 4 days later. Combining a standard method of pollination with the silk channel method of inoculation resulted in reproducible, high levels of pollination and infection in controls. Seventy-seven percent of the kernels on ears pollinated only were fertilized, and 75% of the kernels on ears inoculated only were smutted. Ears pollinated 4 days before inoculation developed only 20% smutted kernels on average, with nearly all tumors forming at the tip of the ear where pollination was probably ineffective. Ears that were inoculated 4 days before pollination were 73% smutted, with only 8% average successful fertilization. Microscopic examination of silks after pollination and inoculation treatments indicated that an abscission zone formed at the bases of pollinated silks and may have prevented fungal infection filaments from growing into the ovaries. These results indicated that pollination rendered ovaries more resistant to infection by U. maydis.Key words: Ustilago, corn smut, pollination, resistance.
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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 | 0.000 | 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".