Effects of Foliar Surfactants on Host Plant Selection Behavior of<i>Liriomyza huidobrensis</i>(Diptera: Agromyzidae)
Bibliographic record
Abstract
The pea leafminer, Liriomyza huidobrensis (Diptera: Agromyzidae), is a highly polyphagous insect pest of global distribution. L. huidobrensis feeds and lays its eggs on leaf tissue and reduces crop marketability because of stippling and mining damage. In field insecticide trials, it was observed that stippling was reduced on plants treated with surfactant alone. The objectives of this study were to determine the effect of surfactants on host selection behaviors of female L. huidobrensis and to assess the phytotoxicity of two common surfactants to test plants. The application of the surfactant Sylgard 309 to celery (Apium graveolens) caused a significant reduction in stippling rates. The application of Agral 90 to cucumber leaves (Cucumis sativus) resulted in changes to the amount of effort invested by females in specific host plant selection behaviors, as well as causing a significant reduction in the amount of stippling damage. The recommended dose of Sylgard 309 does not induce phytotoxicity on celery over a range of age classes nor does Agral 90 cause a phytotoxic effect in 35-d-old cucumber. Thus, reductions in observed stippling and changes to host selection behaviors were caused by an antixenotic effect of the surfactant on L. huidobrensis rather than a toxic effect of the surfactant on the plant. The presence of surfactant on an otherwise acceptable host plant seems to have masked host plant cues and prevented host plant recognition. Results indicate that surfactants may be used to reduce leafminer damage to vegetable crops, potentially reducing the use of insecticides.
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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".