Effect of previous crop, tillage, field size, adjacent crop, and sampling direction on airborne propagules of<i>Gibberella zeae/Fusarium graminearum</i>, fusarium head blight severity, and deoxynivalenol accumulation in winter wheat
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
The objective of this study was to determine the relative importance of previous and adjacent crop, tillage, field size, and sampling direction on the number of viable airborne propagules of Gibberella zea/Fusarium graminearum trapped at anthesis, fusarium head blight (FHB) index, percentage of seeds infected with F. graminearum, and deoxynivalenol (DON) accumulation in seed of winter wheat from commercial fields across southwestern Ontario. More viable airborne propagules of G. zeae/F. graminearum were trapped in wheat fields that were planted on corn or wheat stubble than in wheat fields in which previous crops were nonhosts. Previous crop, field size, and tillage interacted to affect the FHB index, DON accumulation, and percentage of seeds infected with F. graminearum; large fields where corn was planted one year previous to wheat with minimum or no tillage had the highest values. Adjacent crops (nonhost, corn, and wheat) affected the number of viable airborne propagules trapped, FHB index, and percentage of seeds infected with F. graminearum. Sampling direction (east–west) did not have a significant effect on any variable. The number of viable airborne propagules trapped at wheat anthesis was not predictive of FHB symptoms or DON accumulation in grain.
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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 | 0.000 | 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) | 0.000 | 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