Firebrats, <i>Thermobia domestica</i>, aggregate in response to the microbes <i>Enterobacter cloacae</i> and <i>Mycotypha microspora</i>
Bibliographic record
Abstract
Abstract The firebrat, Thermobia domestica (Packard) (Thysanura: Lepismatidae), aggregates in response to the faeces of conspecifics as well as shelters previously inhabited by conspecifics. Our objective was to determine the source of the aggregation signal. Filter paper previously exposed to firebrats induced strong arrestment of firebrats. Polar solvents (water, methanol, acetonitrile) and less polar solvents (hexane, dichloromethane, ethyl ether), alone or in combination, failed to extract the aggregation signal from firebrat‐exposed paper. Moreover, solvent‐extracted paper continued to induce firebrat arrestment. In contrast, the aggregation signal could be obtained by physical extraction (freeze/thawing or ultrasonication) of firebrat‐exposed paper submerged in water. Five fungal species and four bacterial species were isolated from ultrasonicant solutions on potato dextrose‐, nutrient‐, and GlcNAc‐agar. Of the nine isolated microbes tested, only the fungus Mycotypha microspora Fenner (Mucorales) and the bacterium Enterobacter cloacae (Jordan) Hormaeche & Edwards (Enterobacteriaceae) induced arrestment of firebrats in bioassays. Our data support the conclusion that firebrats do not form aggregations in response to pheromones; instead, they aggregate in the presence of specific microbes or their metabolites.
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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".