Dimensionality and modularity of adaptive variation: Divergence in threespine stickleback from diverse environments
Bibliographic record
Abstract
Abstract In nature, populations are subjected to a wide variety of environmental conditions that affect fitness and induce adaptive or plastic responses in traits, resulting in phenotypic divergence between populations. The dimensionality of that divergence, however, remains contentious. At the extremes, some contend that populations diverge along a single axis of trait covariance with greatest availability of heritable variation, even if this does not lead a population directly to its fitness optimum. Those at the other extreme argue that selection can push populations towards their fitness optima along multiple phenotype axes simultaneously, resulting in divergence in numerous dimensions. Here, we address this debate using populations of threespine stickleback ( Gasterosteus aculeatus ) in the Cook Inlet region of southern Alaska from lakes with contrasting ecological conditions. We calculated effective dimensionality of divergence in several trait suites (defensive, swimming, and trophic) thought to be under correlated selection pressures, as well as across all traits. We also tested for integration among the trait suites and between each trait suite and the environment. We found that populations in the Cook Inlet radiation exhibit dimensionality of phenotype high enough to preclude a single axis of divergence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".