Differentiation of Sympatric Arctic Char Morphotypes Using Major Histocompatibility Class II Genes
Bibliographic record
Abstract
Abstract Arctic CharSalvelinus alpinushave colonized northern postglacial lakes within the last few thousand years. Divergent populations have adapted to thrive in the prevailing oligotrophic environments and thus have developed morphotypes with different ecological behaviors. The morphotypes usually differ in size, morphology, coloration, feeding ecology, and/or habitat occupancy. Although morphotypes that have very divergent spawning seasons should become genetically segregated, genetic differentiation, in most cases, has been weak. Thus, results to date have suggested that Arctic Char morphotype separation has been driven largely by the environmentally mediated phenotypic plasticity of the species, with differentiation between morphotypes having commenced too recently to generate substantial genetic drift. Here we used the major histocompatibility (MH) class II genes in an attempt to isolate sympatric Arctic Char morphotypes known to be ecologically differentiated. These morphotypes are from postglacial lakes in both Siberia and eastern Canada, and differ in either diet, habitat occupancy, or both. The MH Class II allelic polymorphism was significantly different between morphotypes. This suggested there is differential heritable adaptation to the natural selection exerted by pathogens unique to each ecological niche within each lake.
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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 | 0.000 |
| 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) | 0.001 | 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".