Using Autumnal Trap Crops to Manage Tarnished Plant Bugs (Lygus lineolaris)
Bibliographic record
Abstract
For insects, surviving winter depends on their capacity to store enough energy and find proper hibernation sites. A common strategy is to minimize movement and hibernate near autumn food sources. We investigated the efficiency of autumnal hosts to act as trap crops where insects could be exposed to targeted repressive treatments. This approach could reduce the local populations of insect pests in the next production season, reducing the need for insecticides. First, we tested the mullein plant’s attractiveness as an autumn trap crop for Lygus lineolaris (Hemiptera: Miridae) in strawberry fields by comparing peak population density among mullein (Verbascum thapsus), strawberry plants (Fragaria × ananassa), buckwheat (Fagopyrum esculentum), and mustard (Sinapis alba). Second, we tested four treatments applied to the autumn trap crops to reduce L. lineolaris winter survivorship: (1) hot water, (2) a pathogen (Beauveria bassiana), (3) insecticide (cypermethrin), and (4) a control. The density of the L. lineolaris population on mullein in autumn and on buckwheat in summer was higher than on strawberry and mustard. Of the overwintering L. lineolaris, 0% survived the winter when treated with the insecticide cypermethrin, while 38.3% survived in the control treatment (without repressive treatment). The B. bassiana and hot water treatments did not differ from the control. The mullein autumn trap crops combined with insecticide treatments could contribute to reducing the overwintering population, hence potentially reducing population during the following growing season.
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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".