Data from: Genetic divergence in morphology-performance mapping between Misty Lake and inlet stickleback
Bibliographic record
Abstract
Different environments should select for different aspects of organismal performance, which should lead to correlated divergence in morphological traits that influence performance. The result should be genetic divergence in aspects of performance, morphology, and associations (“maps”) between morphology and performance. Testing this hypothesis requires quantifying performance and morphology in multiple populations after controlling for environmental differences, but this is rarely done. We used a common-garden experiment to examine morphology and several aspects of swimming performance within and between the lake and inlet populations of threespine stickleback (Gasterosteus aculeatus) from the Misty system, Vancouver Island, Canada. Controlling for body size, lake stickleback had shallower bodies, larger caudal fins, and smaller pelvic girdles. With or without morphological covariates, lake stickleback showed greater performance in both sustained and burst swimming. In contrast, inlet stickleback had greater maneuverability than lake stickleback in some analyses. Morphology-performance relationships were decoupled when considering variation within versus between populations. Moreover, morphology-performance mapping differed between the two populations. Based on these observations we advance an hypothesis for why populations adapting to different environments should show adaptive genetic divergence in morphology-performance mapping.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".