Influence of feeding black soldier fly (<i>Hermetia illucens</i>), cricket (<i>Gryllodes sigillatus</i>), and superworm (<i>Zophobas morio</i>) on the gut microbiota of rainbow trout (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
AIM: This study investigates how replacing fishmeal and fish oil with insect meals in feed impacts the gut microbiota in rainbow trout (Oncorhynchus mykiss), a crucial species in aquaculture. METHODS AND RESULTS: Dietary inclusion of black soldier fly (Hermetia illucens), cricket (Gryllodes sigillatus), and superworm (Zophobas morio) were evaluated for their impact on intestinal microbial diversity and community composition following a 12-week feeding trial. Fish were fed one of four isonitrogenous and isoenergetic diets: a control diet without insect meal, and diets with 15% defatted black soldier fly meal, full-fat adult cricket meal, or full-fat superworm meal. The microbiota of intestinal digesta and fish feed was characterized using 16S rRNA gene sequencing on the Illumina MiSeq platform. Results revealed significantly lower alpha diversity indices in the cricket treatment compared to the control. Beta diversity analysis showed Bacillota as the dominant phylum across all treatments, with the initial stock population richer in Mycoplasmatota. A novel genus within Mycoplasmataceae was prevalent at Day 0 and in all treatments. Black soldier fly meal increased an unidentified Peptostreptococcaceae genus (bsv123) compared to control and superworm diets, while cricket meal elevated Streptococcus levels. CONCLUSIONS: Insect-based diets, particularly with black soldier fly meal, significantly alter beta diversity within the gut microbiota of rainbow trout, with cricket meal reducing alpha diversity and superworm having minimal impact.
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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".