Microbiome profiling suggests novel endosymbiont associations of insect pests of stored grain
Bibliographic record
Abstract
Many arthropods, including economically important pests of stored grains, host intracellular bacterial symbionts. These symbionts can have diverse impacts on host morphology, stress tolerance, and reproductive success. The ability to rapidly determine the infection status of host insects and the identity of intracellular symbionts, if present, is vital to understanding the biology and ecology of these organisms. We used a microbiome profiling method based on amplicon sequencing to rapidly screen 35 captive insect colonies. This method effectively revealed single and mixed infections by intracellular bacterial symbionts, as well as the presence or absence of a dominant symbiont, when that was the case. Because no a priori decisions are required about probable host-symbiont pairing, this method is able to quickly identify novel associations. This work highlights the frequency of endosymbionts, indicates some unexpected pairings that should be investigated further, such as dominant bacterial taxa that are not among the canonical genera of endosymbionts, and reveals different colonies of the same host insect species that differ in the presence and identity of endosymbiotic bacteria.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".