Evidence for genetic distinction among sympatric ecotypes of Arctic char (<i><scp>S</scp>alvelinus alpinus</i>) in south‐western <scp>A</scp>laskan lakes
Bibliographic record
Abstract
Abstract Resource polymorphism may play an important role in the process of speciation. The Arctic char (Salvelinus alpinus) exhibits great phenotypic and genetic diversity across its range, making it an ideal species for studies of resource polymorphism and divergence. Here, we investigated genetic variation at 11 microsatellite loci among 287 Arctic char from five isolated yet proximate postglacial lakes in south‐western Alaska that were previously examined for resource polymorphism. Significant differences in pairwise FST were detected among all lakes (range from 0.05 to 0.28, all P < 0.02). In one lake (Lower Tazimina Lake), we found evidence for two genetic groups of char and for significant differences in the distribution of microsatellite variability among at least two of the three previously described body size morphotypes (‘large’‐, ‘medium’‐, and ‘small’‐bodied char; maximum FST = 0.09; differences in admixture proportions). We also found a significant association between genetic admixture proportions and gill raker counts among body size morphs (r = −0.73, P < 0.001). Our data represent the first record of genetically distinct sympatric morphs of Arctic char in Alaska and provide further evidence that differences in morphology associated with feeding (gill rakers) and growth trajectories reflect niche diversification and promote genetic divergence in Holarctic populations of Arctic char.
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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 | 0.001 | 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".