Bibliographic record
Abstract
The two important Fusarium ear rots of corn, Gibberella ear rot (Fusarium graminearum, formally F. moniliforme and allied species) and Fusarium ear rot (F. verticillioides and allied species) grow under different environmental conditions. F. graminearum grows well only between 26 and 28 degrees C and requires rain both at silking and during disease progression. F. verticillioides grows well at higher temperatures, and ear rot and fumonisin accumulation are associated with drought and insect stress and growing hybrids outside their areas of adaptation. In southern Transkei, where esophageal cancer has been associated with the consumption of F. verticillioides and fumonisin-contaminated corn, environmental conditions favor this fungus in most years. In the nearby areas where the soils, crops, food consumption, and populations are the same and where esophageal cancer is low, temperatures are cooler and F. graminearum is favored. Although F. verticillioides is associated with a disease of corn, it may be that this fungus is a mutualistic endophyte of the plant. Perhaps because of this, breeding for resistance to Fusarium ear rot has produced inconclusive results to date. The best available strategies for reducing the risk of fumonisin contents of maize are to ensure that hybrids are adapted to the environment and to limit drought stress and insect herbivory. It may also be necessary to make use of alternative strategies such as producing hybrids that contain enzymes to degrade fumonisin as it is produced.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".