Characterizing phenotypic diversity in marine populations of threespine sticklebacks
Bibliographic record
Abstract
The threespine stickleback (Gasterosteus aculeatus) is an important model for studying evolution. Sticklebacks are widely distributed in the northern hemisphere and inhabit freshwater, brackish, and marine waters. Anadromous and marine populations (hereafter marine) are assumed to be homogenous in space and invariant in time in their phenotypic characteristics, despite marine environments varying on regional and local scales. Recent studies suggest there is in fact genetic and phenotypic structure in marine sticklebacks, yet the ecological causes remain unclear. My goal was to assess trait variation in marine stickleback populations around Southern coastal British Columbia (BC), and to determine whether or not oceanographic and habitat characteristics explained this variation. The area around Vancouver Island was ideal because four distinct oceanographic regions surround the island with varying coastal habitat types. Between May-July 2019, I sampled ~600 sticklebacks from 15 sites. I then characterized trait variation using two-dimensional (2D) geometric morphometric analysis to compare individuals between oceanographic regions and coastal habitats. I focused on five traits: armour phenotype, head size, body size, head shape, and body shape. I chose these traits because they are ecologically important and well-studied in freshwater populations, where their ecological drivers are known. I found that marine sticklebacks did vary morphologically among and between regions and habitats, but the variation was not immediately related to environmental variation, nor obviously comparable to variation in freshwater populations. Sexual dimorphism was the largest source of variation in the data, a well-established finding. But oceanographic and habitat variables influenced differences between males and females. I concluded that marine sticklebacks offer abundant opportunities for expanding our knowledge of drivers of morphology in nature.
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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 | 0.001 | 0.001 |
| 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 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".