Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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.
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".