Neutral genetic variation in adult Chinook salmon ( <i>Oncorhynchus tshawytscha</i> ) affects brain-to-body trade-off and brain laterality
Bibliographic record
Abstract
Low levels of heterozygosity can have detrimental effects on life history and growth characteristics of organisms but more subtle effects such as those on trade-offs of expensive tissues and morphological laterality, especially of the brain, have not been explicitly tested. The objective of the current study was to investigate how estimated differences in heterozygosity may potentially affect brain-to-body trade-offs and to explore how these heterozygosity differences may affect differential brain growth, focusing on directional asymmetry in adult Chinook salmon ( Oncorhynchus tshawytscha ) using the laterality and absolute laterality indices. Level of inbreeding was estimated as mean microsatellite heterozygosity resulting in four ‘inbreeding level groups’ (Very High, High, Medium, Low). A higher inbreeding level corresponded with a decreased brain-to-body ratio, thus a decrease in investment in brain tissue, and also showed a decrease in the laterality index for the cerebellum, where the left hemisphere was larger than the right across all groups. These results begin to show the role that differences in heterozygosity may play in differential tissue investment and in morphological laterality, and may be useful in two ways. Firstly, the results may be valuable for restocking programmes that wish to emphasize brain or body growth when crossing adults to generate individuals for release, as we show that genetic variation does affect these trade-offs. Secondly, this study is one of the first examinations to test the hypothesized relationship between genetic variation and laterality, finding that in Chinook salmon there is potential for an effect of inbreeding on lateralized morphology, but not in the expected direction.
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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".