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Record W2320622639 · doi:10.1242/jeb.094789

Stinky secretions for keeping clean

2014· article· en· W2320622639 on OpenAlexaff
Katie E. Marshall

Bibliographic record

VenueJournal of Experimental Biology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyZoologyEcology

Abstract

fetched live from OpenAlex

Earwigs are not nature's most cuddly animals. If attacked, they defend themselves with a large set of pincers (called cerci) on the ends of their abdomen. If they're disturbed enough, they'll ooze a smelly liquid, which is occasionally bright yellow, from their abdomen. Despite this, they'll often spend the winter snuggled up together.Living in large aggregations can facilitate the spread of disease, so PhD student Tina Gasch and her colleagues at Justus Liebig University Giessen in Germany set out to see whether the earwigs' chemical secretions could be used to combat the microorganisms found in their environments. They collected three different earwig species – Apterygida media, Chelidurella guentheri and Forficula auricularia – to examine the chemical composition of their defensive ooze and to investigate whether it might kill pathogens.First, the researchers dissected out the sacs that held the defence liquid and then used gas chromatography coupled to mass spectroscopy to identify the chemical components. They found that the secretions of all three earwig species were chemically quite simple, with no more than four unique compounds found in any species' gunge. One compound, 2-ethyl-3-methyl-1,4-benzoquinone, had never been found in insects before, but it is used as a defensive compound by harvestmen arachnids.Next, the researchers set out to challenge the ooze with a shooting gallery's worth of potential pathogens to see whether the earwigs were mounting a chemical defence when they secreted their defensive compounds. Gram-positive and gram-negative bacteria, two species of fungi and the nematode Caenorhabditis elegans were all doused with the defensive fluid from F. auricularia, which turned out to be an effective pathogen slayer.Finally, the researchers wanted to know whether the smell from natural aggregations of F. auricularia contained the chemicals found in the defensive emissions. They placed bamboo traps containing swabs that absorbed volatiles in areas where the earwigs liked to aggregate. Analysing the swabs, the team found that the characteristic odour of huddled F. auricularia groups contains the defensive chemicals that they found in the ooze.While we might not like it much, the stink of the defensive ooze might be the ‘sweet, sweet smell of home’ for earwigs, which provides them with a sterile space free of pathogens.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.013

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 head0.016
GPT teacher head0.307
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

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