Effect of inoculation time and point of entry on disease severity in <i>Fusarium graminearum</i> , <i>Fusarium verticillioides</i> , or <i>Fusarium subglutinans</i> inoculated maize ears <sup>1</sup>
Bibliographic record
Abstract
To determine if differences exist in the ability of three Fusarium species (F. graminearum, F. verticillioides, and F. subglutinans) to infect maize ears as the silks and kernels mature, one moderately resistant and two susceptible hybrids were inoculated at two points of entry (silk channel and kernels) in 1994 and 1995. Inoculations were conducted nine times for each part of the ear starting from silk emergence. For all three species, the greatest silk channel inoculated disease severities occurred when the ears were inoculated in the early stages of silk development, with a peak in susceptibility around 1-6 days after silk emergence, followed by a rapid decrease in severity. With kernel inoculations, a general decrease in disease severity occurred with time for all species. Fusarium verticillioides had the lowest disease severity of the three species. With silk channel inoculations, F. subglutinans resulted in higher disease severity than F. graminearum; however, the opposite was found with kernel inoculations, with F. graminearum producing the greatest amount of disease symptoms.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".