Effect of chemical complexity of essential oils on feeding deterrence in larvae of the cabbage looper
Bibliographic record
Abstract
The relationship between chemical composition and feeding deterrent activity of seven essential oils is explored, including those from Syzygium aromaticum (L.) Merr. & Perry (Myrtaceae), Cinnamomum zeylanicum Blume (Lauraceae), Lavendula latifolia (L.) Medicus, Lavendula angustifolia L., Mentha crispa L., Mentha arvensis L. and Mentha piperita L. (Lamiaceae) against an important agricultural pest, the cabbage looper ( Trichoplusia ni Hübner, Noctuidae), using a leaf‐disc choice bioassay. Comparison of the deterrent activity of ‘full mixtures' with respective artificial blends missing individual constituents demonstrates that, for most oils, minor constituents in a mixture can be as important as major constituents with respect to the overall feeding deterrent effect. There is a lack of correlation between the feeding deterrent effect of an individual constituent of an essential oil and its contribution to the overall activity of the ‘full mixture’ in some cases. The effect of removing an individual constituent from the mixture of an essential oil depends on the unique properties of the mixture, which in turn may reflect the interaction of its constituents. Understanding the role and contribution of each constituent to the overall activity of the oil can facilitate the creation of artificial blends that optimize their efficacy against different pests.
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".