A TEST FOR THE GENETIC BASIS OF NATURAL SELECTION: AN INDIVIDUAL-BASED LONGITUDINAL STUDY IN A STREAM-DWELLING FISH
Bibliographic record
Abstract
In addition to the well-studied evolutionary parameters of (1) phenotype-fitness covariance and (2) the genetic basis of phenotypic variation, adaptive evolution by natural selection requires that (3) fitness variation is effected by heritable genetic differences among individuals and (4) phenotype-fitness covariances must be, at least in part, underlain by genetic covariances. These latter two requirements for adaptive evolutionary change are relatively unstudied in natural populations. Absence of the latter requirements could explain stasis of apparently directionally selected heritable traits. We provide complementary analyses of selection and variation at phenotypic and genetic levels for juvenile growth rate in brook charr Salvelinus fontinalis in Freshwater River, Newfoundland, Canada. Contrary to the vast majority of reports in fish, we found very little viability selection of juvenile body size. Large body size appears nonetheless to be selectively advantageous via a relationship with early maturity. Genetic patterns in evolutionary parameters largely reflected phenotypic patterns. We have provided inference of selection based on longitudinal data, which are uncommon in high fecundity organisms. Furthermore we have provided a practicable framework for further studies of the genetic basis of natural 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".