Floral attractants for the female soybean looper, <i>Thysanoplusia orichalcea</i> (Lepidoptera: Noctuidae)
Bibliographic record
Abstract
BACKGROUND: The soybean looper, Thysanoplusia orichalcea (F.), is a polyphagous insect pest of vegetable crops. Indonesian in origin, it has spread to Europe, India, Africa, Australia and New Zealand. The identification of an attractant for female T. orichalcea could enable the development of alternative pest management strategies to those provided by insecticides or sex pheromones, which are often only attractive to males. RESULTS: Traps baited with synthetic lures derived from Canada thistle, Cirsium arvense (L.) Scop., floral volatiles attracted female T. orichalcea. Phenylacetaldehyde, a floral compound attractive to many Lepidoptera and present in C. arvense, was tested alone as an attractant for the soybean looper and caught significantly more female than male T. orichalcea. Trap catch was greatest when phenylacetaldehyde was combined with five prevalent volatiles present in C. arvense headspace collections: 2-phenylethyl alcohol, methyl salicylate, dimethyl salicylate, benzaldehyde and benzyl alcohol. Twice as many female moths as males were collected. CONCLUSION: Successful trapping of female T. orichalcea in either a lure-and-kill or a mass trapping system may offer an effective way to manage its population size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".