Differences in Aceria tosichella population responses to wheat resistance genes and wheat virus transmission
Bibliographic record
Abstract
Abstract Severe winter wheat yield losses due to infestations of wheat curl mite,Aceria tosichellaKeifer, and mite-transmitted viruses occur in wheat production areas of the United States and Canada. Mite infestation alone causes stunted, chlorotic plants in susceptible wheat varieties, and mites transmitWheat Streak Mosaic(WSMV),High Plains Wheat Mosaic(HPWMoV), andTriticum Mosaic Virus(TriMV). Wheat curl mites were collected from 25 sites in Kansas, Missouri, Nebraska, Texas, North Dakota, and South Dakota in 2014 and 2015. At each site, mite virulence was determined to wheat plants harboring theCmc2-,Cmc3-, orCmc4mite resistance gene; orCmc4plus theWsm2WSMV resistance gene. Mites collected from 92%, 36%, and 24% of sites were virulent to susceptible Jagger wheat plants (noCmc),Cmc2, andCmc3,respectively. The mega-population consisting of all 25 mite sub-populations was avirulent to 80% of plants containingCmc4 + Wsm2orCmc4. WSMV, HPWMoV, or TriMV was present in mites at 76%, 16%, and 8% of the 25 sites, respectively. Our results will enable breeders to increase the efficiency of wheat production by releasing wheat varieties containing wheat curl mite resistance genes that reduce wheat yield losses.
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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".