Development of early maturity maize hybrids for resistance toFusarium andAspergillus ear rots and their associated mycotoxins
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maize is mainly affected by two fungal pathogens, Fusarium verticillioides and Aspergillus flavus , causing Fusarium ear rot (FER) and Aspergillus 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 by F. verticillioides and A. 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 F 1 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 of F. verticillioides and A. 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it