Natural Compounds as Spider Repellents: Fact or Myth?
Bibliographic record
Abstract
Although some spiders are globally invasive, found at high densities, and may be considered pests (particularly those that are toxic to humans), there are few pest management methods based on experimental data. 'Common wisdom' and advertisements on internet websites assert that a number of natural substances repel spiders. We tested whether the three substances cited most frequently (lemon oil, peppermint oil, and chestnut-fruits) effectively repelled female spiders or whether these were myths. We presented each of the putative repellents versus a control in a two-choice assay and tested responses of females of three invasive spider species in two different families: theridiids, Latrodectus geometricus C. L. Koch (Araneae: Theridiidae) and Steatoda grossa C. L. Koch (Araneae: Theridiidae) and the araneid, Araneus diadematus Clerck . Chestnuts (Araneae: Araneidae) and mint oil strongly repelled L. geometricus and A. diadematus. S. grossa was less sensitive to these chemicals but had a slight tendency to avoid chestnuts. However, lemon oil, the substance most likely to be cited as a repellent (over 1,000,000 hits on Google), had no effect on any of these spiders. We conclude that volatiles released by mint oil and chestnuts may be effective in deterring spider settlement in two different families of spiders, but lemon oil as a repellent is a myth.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".