Effect of temperature, rainfall and planting date on aflatoxin and fumonisin contamination in commercial Bt and non-Bt corn hybrids in Arkansas
Bibliographic record
Abstract
Corn (maize, Zea mays) is susceptible to contamination with aflatoxins, fumonisins and other mycotoxins, particularly in the southeastern USA. In principle, mycotoxin contamination could be reduced in commercial corn hybrids with shorter growing seasons by planting at dates which minimize plant stress during the critical kernel-filling period. To evaluate this strategy, commercial Bt and non-Bt hybrids were planted in Arkansas in mid-April and early May of 2002, 2004 and 2005. The mid-April planting date resulted in lower aflatoxin contamination in harvested corn each yr and in significantly less frequent contamination above a regulatory action level in 2005 and overall than did the early-May planting date in both Bt and non-Bt corn. The mid-April planting date resulted in significantly lower total fumonisin contamination in harvested corn and in less frequent contamination above a regulatory advisory level than the early May planting date in 2 of 3 yr and overall in both Bt and non-Bt corn. All fumonisin subtypes studied were reduced. Frequent co-occurrence of aflatoxin and fumonisin was observed. Fumonisin levels averaged lower in Bt hybrids than in non-Bt hybrids at all plantings. Reduced aflatoxin and fumonisin contamination with mid-April planting could not be explained by any measure of heat stress during the kernel-filling period.
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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".