Environmental and Host Characteristics Shape the Gut Microbiota of the Sand Field Cricket, Gryllus firmus
Bibliographic record
Abstract
The gut microbiota plays an essential role in its host’s nutrition, development and behavior. Although crickets are becoming major ecosystemic model systems and have important societal applications, such as alternative animal proteins or biocatalysts, little is known about their gut microbiome acquisition and how environmental factors shape this community. Therefore, in this study, we exposed sand field crickets to soils with different characteristics and microbial communities to test the influence of these on gut microbial community composition. We used 16S/18S rRNA gene Illumina sequencing to analyze different soil and gut communities, targeting the three domains of life, Archaea, Bacteria, and Eukaryotes. Our results showed a dominance of Mucoromycota fungi and Bacteroidota in the gut microbiota. We were unable to retrieve sufficient read numbers for the Archaea. Most of the microbial taxa that were identified can degrade soil-derived complex organic matter, likely helping the host digest its food. The soil characteristics had a significant impact on the gut microbial community structure, supporting our assumption that the environment plays an essential role in gut microbiota acquisition. Host sex also had an impact on the gut community, possibly because the female guts were bigger in mass, leading to differences in oxygen concentrations.
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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".