Sex‐biased genetic component distribution among populations: additive genetic and maternal contributions to phenotypic differences among populations of Chinook salmon
Bibliographic record
Abstract
An approach frequently used to demonstrate a genetic basis for population-level phenotypic differences is to employ common garden rearing designs, where observed differences are assumed to be attributable to primarily additive genetic effects. Here, in two common garden experiments, we employed factorial breeding designs between wild and domestic, and among wild populations of Chinook salmon (Oncorhynchus tshawytscha). We measured the contribution of additive (V(A)) and maternal (V(M)) effects to the observed population differences for 17 life history and fitness-related traits. Our results show that, in general, maternal effects contribute more to phenotypic differences among populations than additive genetic effects. These results suggest that maternal effects are important in population phenotypic differentiation and also signify that the inclusion of the maternal source of variation is critical when employing models to test population differences in salmon, such as in local adaptation studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".