Optimizing Biomass Production in Aquaculture: Implementing Triploidy and Probiotic Therapies in Hatchery Reared Chinook Salmon (Oncorhynchus tshawytscha)
Bibliographic record
Abstract
Aquaculture producers seek to increase the yield and quality of fish for sale and consumption. Triploidization can result in improved flesh quality and larger body size due to redirecting energy that would otherwise be invested in reproductive efforts. Here, we used probiotic therapies to potentially further enhance the growth of hatchery-reared Chinook salmon (Oncorhynchus tshawytscha) and investigated how triploidy and probiotics influenced fish mass directly and also via fish transcriptomic and behavioural profiles at the juvenile life stage. Specifically, we measured transcriptomic- (early neural development, long-term neural development, metabolism/appetite, growth, and stress response function genes) and behavioural profiles (consisting of fish that are reactionary, active, bold/aggressive, or exhibiting extremes in locomotive behaviours) of juvenile Chinook salmon under our ploidy- and feed treatments. Overall, triploid individuals had lower mass compared to diploid counterparts, counter to our predictions, and triploidy indirectly impacted mass via Shh (early neural development) gene transcription and a bold/aggressive behavioural profile. Further, probiotics did not directly impact mass, but rather worked through a genomic mechanism (i.e., growth gene p53) to do so. A few of the genes examined (i.e., Auts2, CRF, IGF-1, and p53 from early neural response, stress response, and two from growth and functional groups, respectively) exhibited increases in transcription coinciding with increased mass of individuals, independent of other variables. Ultimately, our study provides evidence that using a behavioural transcriptomics approach is useful in uncovering relationships between gene transcription, behavioural profiles, and growth-related traits (i.e., mass).
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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".