ddRAD genotyping reveals hierarchical genetic population structure in anadromous Arctic char (<i>Salvelinus alpinus</i>) in the Lower Northwest Passage, Nunavut
Bibliographic record
Abstract
Contemporary intraspecific patterns of genetic variation reflect the historical effects of population subdivision–expansion and forces like drift, gene flow, and selection. We investigated the population structure of anadromous Arctic char (Salvelinus alpinus) in the Lower Northwest Passage (LNWP), Nunavut, using 3074 genome-wide single nucleotide polymorphisms markers (SNPs). Overall, the genetic differentiation was weak to moderate among 18 sampling locales (global FST = 0.037). Populations were structured hierarchically, with a deeper genetic division between King William Island (global FST = 0.018) and mainland populations ∼200 km away to the south (global FST = 0.018), and some evidence of genetic subgroups within the former. These findings suggest a role for char migratory behaviour in shaping contemporary genetic population structure, with demographic modelling favouring an isolation with migration over a strict isolation scenario. Twenty-two SNPs were identified as potentially under divergent selection with putative functions including neurotransmission and bone development and growth during late embryogenesis. Our study is the first survey of Arctic char in the LNWP using genomics and provides baseline data for the development of a sustainable fishery within this region.
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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) | 0.000 | 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".