Population Genetic Analyses of Arctic Char (Salvelinus Alpinus) Life History Types in Nettilling Lake & Amadjuak River Ecosystem: A Test of Reproductive Isolation
Bibliographic record
Abstract
A great number of studies have identified strong genetic differences between sympatric anadromous and resident populations of Salmonidae. However, Arctic char (Salvelinus alpinus) migratory phenotypes in the Nettilling Lake and Amadjuak River ecosystem in Nunavut, Canada have not been genetically characterized, and it remains unclear if distinct genotypes and phenotypes associated with migratory life history differences are maintained through reproductive isolation, and they have been assumed to be sympatric populations, or co-occurring populations. Co-occurring Arctic char (n=225) were sampled from eleven sites along the Amadjuak River in 2014 and 2015. Twelve microsatellite loci were used to quantify genetic variation among the sampled fish. The genetic data showed two genetic clades (populations) of Arctic char living in the ecosystem. However, each genetic population contained both resident and anadromous individuals (migratory life histories). These results suggest that genotype should be considered when identifying populations of Arctic char for conservation and management purposes. Fish from the two different clades were captured at the same site, indicative of possible sympatry, increasing the complexity of effective management of this important fishery resource. We thus suggest using genetic methods to categorize individual fish to their respective genetic population, while further work should be done to explore morphological and physiological trait differences to simplify the management of the fish from the two cryptic populations.
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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 | 0.001 | 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".