Offspring size effects vary over fine spatio-temporal scales in Atlantic salmon (<i>Salmo salar</i>)
Bibliographic record
Abstract
Classic offspring-size theory predicts that a single level of investment per offspring maximizes parental reproductive success in a given environment. Yet, substantial variation in offspring size is often observed among females within populations. Variation at this scale may occur because spatio-temporal variation in stabilizing selection prevents erosion of genetic variation. We tested whether patterns of size-specific offspring survival of Atlantic salmon (Salmo salar) varies across location and season within a short stretch of a natural stream by manipulating the emergence timing of juveniles from 12 families with different mean egg sizes and assessing their performance at two locations. The relationship between egg size and juvenile survival varied temporally and spatially; large eggs were advantageous for early emergers in one location, whereas egg size had no effect in the other. Furthermore, the performance of later emerging juveniles did not depend on egg size in either location, possibly because the early emergers had grown or established territories. Thus, selection on offspring size can be complex and vary across short periods of time and small geographic distances, thereby preventing the erosion of genetic variation expected under consistent stabilizing selection.
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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 | 0.001 |
| 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".