Transcriptomic Responses of Atlantic Salmon (Salmo salar) to Environmental Enrichment during Juvenile Rearing
Bibliographic record
Abstract
Captive rearing programs (hatcheries) are often used in conservation and management efforts for at-risk salmonid fish populations. However, hatcheries typically rear juveniles in environments that contrast starkly with natural conditions, which may lead to phenotypic and/or genetic changes that adversely affect the performance of juveniles upon their release to the wild. Environmental enrichment has been proposed as a mechanism to improve the efficacy of population restoration efforts from captive-rearing programs; in this study, we examine the influence of environmental enrichment during embryo and yolk-sac larval rearing on the transcriptome of Atlantic salmon (Salmo salar). Full siblings were reared in either a hatchery environment devoid of structure or an environment enriched with gravel substrate. At the end of endogenous feeding by juveniles, we examined patterns of gene transcript abundance in head tissues using the cGRASP-designed Agilent 4×44K microarray. Significance analysis of microarrays (SAM) indicated that 808 genes were differentially transcribed between the rearing environments and a total of 184 gene ontological (GO) terms were over- or under-represented in this gene list, several associated with mitosis/cell cycle and muscle and heart development. There were also pronounced differences among families in the degree of transcriptional response to rearing environment enrichment, suggesting that gene-by-environment effects, possibly related to parental origin, could influence the efficacy of enrichment interventions.
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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".