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Record W2780619983 · doi:10.1093/jee/tox339

Natural Compounds as Spider Repellents: Fact or Myth?

2017· article· en· W2780619983 on OpenAlexafffund
Andreas Fischer, Manfred Ayasse, Maydianne C. B. Andrade

Bibliographic record

VenueJournal of Economic Entomology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsThe Scarborough HospitalUniversity of TorontoSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaUniversität Ulm
KeywordsTheridiidaeSpiderBiologyToxicologyBotanyZoology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.070
GPT teacher head0.287
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2017
Admission routes2
Has abstractyes

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