Fungal symbiont of firebrats (Thysanura) induces arrestment behaviour of firebrats and giant silverfish but not common silverfish
Bibliographic record
Abstract
Abstract We have recently shown that firebrats, Thermobia domestica (Packard) (Thysanura: Lepismatidae), carry, and deposit with their faeces, the symbiotic bacterium Enterobacter cloacae (Jordan 1890) Hormaeche and Edwards 1960 (Enterobacteriaceae) and the symbiotic fungus Mycotypha microspora Fenner, 1932 (Mycotyphaceae), and that these microbes induce arrestment behaviour and aggregation of firebrats. Here, we tested whether giant silverfish, Ctenolepisma longicaudata Escherich (Thysanura: Lepismatidae), and common silverfish, Lepisma saccharina (Linnaeus) (Thysanura: Lepismatidae), also arrest in response to these two microbes. In dual-choice bioassays, E. cloacae arrested firebrats but not giant silverfish or common silverfish, whereas M. microspora arrested firebrats and giant silverfish but not common silverfish. As close relatives, firebrats and giant silverfish have similar microclimate and nutrient requirements and may use M. microspora as the same aggregation cue when they aggregate in hot and humid microclimates where M. microspora proliferates and breaks down cellulose. As a more distant relative to firebrats and giant silverfish, common silverfish seem to require a different as yet unknown aggregation cue or signal, possibly one that is indicative of the type of microclimate (room temperature; high humidity) they prefer.
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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 | 0.000 | 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".