Examining Probiotic Therapy on Triploid Chinook Salmon ( <i>Oncorhynchus tshawytscha</i> ): A Behavioural Genomics Approach
Bibliographic record
Abstract
The aquaculture industry accounts for nearly half of the world's fish for consumption. To reduce energy expenditures towards reproductive efforts, hatcheries can induce the process of triploidization in their stock (resulting in altered ploidy from 2N to 3N). This process induces an early life stressor at the egg stage, yet can have several benefits (e.g., greater growth potential, producing sterile fish, and lowered aggression), as well as drawbacks (e.g., compromised immune function, increased stress reactions with lowered behavioural/physiological responsiveness, and greater environmental sensitivities). In this study, implementation of probiotic therapy is examined to determine its viability for overcoming drawbacks of triploidization. By adding probiotics into feed, we expect behavioural changes as well as alterations to the gut‐brain axis, which result in further changes in brain function and neural gene expression. Behaviours and neural transcriptomic profiles of hatchery‐reared Chinook salmon ( Oncorhynchus tshawytscha ) eight months post‐fertilization are examined following a series of behavioural trials, designed to assess the fish's foraging motivation, competitiveness, sensitivity to stress, and behavioural flexibility. Fish from four treatment groups (2N‐regular feed, 2N‐probiotic feed, 3N‐regular feed, and 3N‐probiotic feed; n=90 per treatment) underwent a behavioural assay consisting of an open‐field, novel‐object, predator‐stimulus and mirror tests. Transcriptional profiles of neural tissues will be examined for genes relating to neural functions (i.e., neurogenesis/synaptic plasticity) to mechanistically examine behavioural flexibility, biomarkers for individual level stress response and development (i.e., growth and appetite). Through a behavioural genomics approach, we will be examining transcriptional expression integrated with behavioural profiles, allowing for determining the role of individual transcriptional variation across different behavioural groups. We will be able to determine how ploidy (specifically triploidization) coupled with probiotic therapies affect the gut‐brain axis via behavioural and transcriptional profiles in an economically important fish species. Ultimately, we will determine whether probiotic therapies are effective for future usage in the aquacultural industry.
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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".