Geographic Distribution of Soybean Aphid Biotypes in the United States and Canada during 2008–2010
Bibliographic record
Abstract
ABSTRACT Soybean aphid ( Aphis glycines Matsumura) is a native pest of soybean [ Glycine max (L.) Merr.] in eastern Asia and was detected on soybeans in North America in 2000. In 2004, the soybean cultivar Dowling was described to be resistant to soybean aphids with the Rag1 gene for resistance. In 2006, a virulent biotype of soybean aphid in Ohio was reported to proliferate on soybeans with the Rag1 gene. The objective was to survey the occurrence of virulent aphid populations on soybean indicator lines across geographies and years. Nine soybean lines were identified on the basis of their degree of aphid resistance and their importance in breeding programs. Naturally occurring soybean aphid populations were collected in 10 states (Kansas, Illinois, Indiana, Iowa, Michigan, Minnesota, North Dakota, Ohio, South Dakota, and Wisconsin) and the Canadian province of Ontario. The reproductive capacity of field‐collected soybean aphid populations was tested on soybean lines; growth rates were compared in no‐choice field cages at each geographic region across 3 yr. The occurrence of soybean aphid biotypes was highly variable from year to year and across environments. The frequency of Biotypes 2, 3, and 4 was 54, 18, and 7%, respectively, from the 28 soybean aphid populations collected across 3 yr and 11 environments. Plant introduction (PI) 567598B, a natural gene pyramid of rag1c and rag4 , had lowest frequency of soybean aphid colonization (18%). Several factors may have contributed to the variability, including genetic diversity of soybean aphids, parthenogenicity, abundance of the overwintering host buckthorn ( Rhamnus spp.), and migratory patterns of soybean aphids across the landscape.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".