Population dynamics of greenbug biotypes “E” and “F” on Texas bluegrass
Bibliographic record
Abstract
Abstract Cool‐season perennial forage grasses are known to serve as alternate hosts of the greenbug, Schizaphis graminum (Rondani) in the southern Great Plains of the USA. Texas bluegrass ( Poa arachnifera Torr.) is a cool‐season perennial forage grass indigenous to this region and because of its persistence under grazing is being bred for improved forage production. An experiment was conducted to determine the population dynamics of greenbug biotype “E”, the predominant biotype found in this region and biotype “F” which is known to infest bluegrass species. Twenty‐five Texas bluegrass genotypes, three Kentucky bluegrass ( Poa pratensis L.) genotypes, a Canada bluegrass ( Poa compressa L.) genotype, two Texas × Kentucky bluegrass hybrids, and a Texas × Canada bluegrass hybrid were screened for their abilities to support greenbug population growth. Bluegrass species and genotypes varied significantly in their ability to support greenbug biotypes E and F. On the basis of higher aphid numbers, biotype E had a higher population growth rate as compared with biotype F on Texas bluegrass. Some Texas bluegrass genotypes were not included in both biotype trials due to lack of availability of uniform plant material for inoculation. Nine of 25 Texas bluegrass genotypes did not support biotype E greenbug population growth. Similarly, 13 of 22 Texas bluegrass genotypes did not support biotype F population growth. Both Canada and Kentucky bluegrass supported high populations of biotype F and interspecific hybrids varied in their host suitability to both biotypes.
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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".