Development of early maturity maize hybrids for resistance toFusarium andAspergillus ear rots and their associated mycotoxins
Bibliographic record
Abstract
Maize is mainly affected by two fungal pathogens,Fusarium verticillioides andAspergillus flavus, causingFusarium ear rot (FER) andAspergillus ear rot (AER), respectively. Both fungi are of concern to stakeholders as they affect crop yield and quality, contaminating maize grains with the mycotoxins fumonisins and aflatoxins. The easiest strategy to prevent pre-harvest contamination byF. verticillioides andA. flavus is to develop maize hybrids resistant to FER and AER, as well as to their associated mycotoxins. The objective of this investigation was to test 46 F1 hybrids, originated from different Italian, US and Canadian breeding groups, for these important traits and their agronomic performances. All hybrids were planted and artificially inoculated with toxigenic strains ofF. verticillioides andA. flavus at two locations in 2017, and the best performing 17 out of 46 were also tested in 2018. Ear rots were present in all hybrids in 2017 and 2018, with percentages ranging from 6.50 to 49.50%, and 5.50 to 45.53%, for FER and AER, respectively. Seven hybrids (PC8, PC15, PC9, PC11, PC14, PC34 and PC17) presented the lowest levels of both diseases considering the overall locations and growing seasons, and three of these (PC8, PC11 and PC14) were also amongst the least mycotoxin contaminated hybrids in 2017. The inbred lines used in hybrid production may provide additional sources of resistance suitable in breeding programs targeting multiple pathogens and their mycotoxins.
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 | 0.000 |
| 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 | 0.001 |
| 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".