Endophyte–grass complexes and the relationship between feeding preference and performance in a grass herbivore
Bibliographic record
Abstract
Abstract Overseeding non‐endophytic turfgrass lawns with endophytic turfgrass is considered promising for the development of integrated pest management programs in urban landscapes. In this context, a better understanding of the variability in the preference–performance response of insect herbivores exposed to endophytic grasses could be useful to develop efficient practices. Specifically, while endophytic varieties that are strongly toxic and avoided could provide good control of mobile pests, varieties that are toxic but not avoided could be more suitable for the control of sedentary pests. In this laboratory study, we investigated how the infection of 10 grass varieties by Neotyphodium endophytes affected the feeding performance and preference of newly hatched nymphs of the hairy chinch bug, Blissus leucopterus hirtus Montandon (Heteroptera: Lygaeidae), a common turfgrass pest in north‐eastern USA. We found that endophytes generally induced a strong relationship between feeding performance and preference in this herbivore. However, two endophytic varieties did not conform to this relationship, with one variety being highly toxic but not avoided and the other less toxic but highly avoided. These results provide a solid basis to further explore the impacts of endophytic grasses on the dispersal and survival of insect pests in mixed stands of endophytic and non‐endophytic plants.
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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".