Genetic and phenotypic differentiation among Galaxias maculatus populations in a Patagonian postglacial lake system
Bibliographic record
Abstract
Understanding the influence of landscape features on population differentiation is fundamental to evolutionary biology studies. We examined spatial patterns of genetic and phenotypic variability among Galaxias maculatus populations in a complex of four postglacial lakes in northwestern Patagonia differing in size and connectivity among them. A hierarchical Bayesian analysis grouped the individuals collected from eleven localities into three genetic clusters, first defining the populations of the two large lakes and separating the two small lakes in subsequent analysis. Genetic structuring was restricted within large lakes. It is known that the larval stage of Galaxias maculatus migrate to the limnetic zone of Patagonian lakes, possibly exerting an homogenizing effect on gene flow within lakes. Gene flow asymmetry and divergences among lakes can be explained by a combination of landscape characteristics and the presence of predators in the short streams that connect them. Individuals from the small lakes are the most divergent morphologically and genetically. The population in the isolated Redonda Lake, exhibits meristic differences as well, suggesting strong drift and environmental effects. This population is likely to have been isolated following the decline in water level of a paleolake that existed in this region approximately 13.2 kya BP.
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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 | 0.000 |
| 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) | 0.000 | 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".