Genetic and environmental correlates of morphological variation in a marine fish: the case of Baltic Sea herring (<i>Clupea harengus</i>)
Bibliographic record
Abstract
Baltic Sea herring (Clupea harengus) have been shown to exhibit morphological differences across the marked salinity and temperature gradients in the region. Here we analyse genetic (nine microsatellite loci), morpho metric (skull shape), and meristic (pectoral fin rays and number of vertebrae) variations across seven samples of spawning herring collected from four spawning locations in the Baltic Sea to examine whether morphological variation correlates with genetic and (or) environmental factors. Results suggest that herring is adapting to its environment through a combination of selection and plastic responses. Skull shape, including and excluding size variation, differed significantly among samples, both temporally and spatially. Genetic and morphometric distances were correlated, especially when size variation was excluded from the analysis. When size variation was included, skull shape variation was more closely correlated with environmental distances among spawning locations. Vertebrate number differed among samples and was correlated with environmental distances, whereas the number of fin rays was not. Genetic and geographic distances among samples were not correlated.
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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 | 0.001 |
| 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".