Examining the Effectiveness of Probiotic Therapy for Improving Growth Performance of Triploid Chinook Salmon (Oncorhynchus Tshawytscha) in Aquaculture Using a Behavioural Genomics Approach
Bibliographic record
Abstract
With an increasing human population, there has been increased production of fish to meet nutritional needs. Commercial aquaculture accounts for a significant portion of seafood production with salmonids being the major farmed finfish in Canada. To obtain greater biomass from aquaculture with minimal drawbacks (e.g., compromised flesh quality), triploidization has been implemented, altering ploidy from 2N to 3N, to induce sterility and promote energy investment towards somatic growth. Triploid individuals experience transcriptional and behavioural changes resulting in disease, mortalities, and reduced growth. Probiotic therapies (live microorganisms) have been recommended to potentially overcome drawbacks of triploidy and improve mass due to the purported benefits to the host. Through a behavioural genomics approach, I examined neural transcriptional profiles (i.e., relating to neural functions, stress response, appetite/metabolism, and growth) and combined these with behavioural profiles via behavioural assays (i.e., open field, novel object, predator, and mirror tests) in hatchery-reared juvenile Chinook salmon (Oncorhynchus tshawytscha). Siblings from 15 families were placed in four treatment groups: 2N-regular feed, 2N-probiotic feed, 3N-regular feed, and 3N-probiotic feed to determine mechanisms driving differential growth. I found no universal effects of treatments on growth. While triploid individuals had reduced mass, growth was influenced by transcription, including interactions between a bold/aggressive behavioural profile and Shh gene transcription. Probiotic therapy (i.e., Lactobacillus, Bifidobacterium, and Lactococcus) had no direct impact on mass, but increased mass when coupled with high gene transcription of p53. Through behavioural genomics, I uncovered important relationships and interactions that remain to be further described.
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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 | 0.001 |
| 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".