Virulence of Soybean Aphid, Aphis glycines (Hemiptera: Aphididae) Clones on Detached Leaves and Whole Plants
Bibliographic record
Abstract
Since the discovery of the invasive soybean aphid, Aphis glycines Matsumura, in the USA in 2000, populations have expanded throughout the Midwestern USA and Canada. Its negative economic impact on soybean [Glycine max (L.) Merr.] yield provides the impetus to continue investigating the effectiveness of host plant resistance which has been affected by soybean aphid biotypes that overcome soybean plants containing Rag (resistance to A. glycines) genes. In this study, 14 aphid clones collected on soybean and buckthorn (Rhamnus cathartica L.) plants along with four known aphid biotypes (from our stock collection) were evaluated in no-choice assays by quantifying the number of aphids on soybean genotypes with Rag genes and the susceptible cultivar Williams 82. No-choice assays were conducted using detached leaves and whole plants. Inconsistent responses of the biotypes from the stock collection by using detached leaves confirm the efficacy of whole plant assays to accurately differentiate biotypes. None of the biotypes or field clones from Illinois, Indiana and South Dakota overcame the resistance of soybean genotypes LD14-8004 (Rag1) and PI437696. A soybean aphid clone from Wooster, Ohio readily colonized LD14-8002 (Rag2), but did not overcome any of the other resistance genes indicating that it is different from biotypes 3 and 4, which also overcome LD14-8002 (Rag2) and, respectively, LD14-8004 (Rag1) and LD14-8006 (Rag3).
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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 | 0.001 |
| 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".