The Gut Microbiome and its Relationship to Migratory Behaviour and Fate in Wild Fishes
Bibliographic record
Abstract
Gut microbiota play an essential role in modulating host physiological processes that contribute to host health and fitness.Wild, migratory species offer a unique opportunity to examine the gut microbiome under an additional layer of complexity.Changing external environments, compounded by migration-associated physiological changes in the host, may be associated with variations in the microbial community and differentially impact fish health and fitness.The objective of this thesis was to investigate the hypothesis that the hindgut microbial communities vary relative to migratory behaviour and fate in wild fishes.Specifically, I assessed hindgut microbial communities in three fish species that exhibit different migratory behaviours using 16S rRNA gene amplicon sequencing: white sucker (Catostomus commersonii), sockeye salmon (Oncorhynchus nerka), and brown trout (Salmo trutta).Further, I highlighted the importance of transitioning to non-lethal sampling methods when studying wild fish microbiomes, especially in relation to studying behaviours.Gut microbial analysis revealed that potamodromous white suckers were dominated by the genus Aeromonas.work would not be possible.I will forever appreciate all the troubleshooting effort put into trying to optimize my samples.I would also like to thank all the members of the Cooke Lab at Carleton University whom I have had the pleasure of working with over the years.I am particularly thankful to B. Hlina and Drs.J. Chapman and M. Lawrence for help and encouragement with R and Dr. L. Elmer for all the field help and fun in BC.
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 | 0.001 |
| 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".