Ancestry, population structure, and conservation genetics of Arctic grayling (Thymallus arcticus) in the upper Missouri River, USA
Bibliographic record
Abstract
We genotyped Arctic grayling ( Thymallus arcticus ) at 10 microsatellite loci in 18 samples (n = 726) from Montana, Wyoming, and Saskatchewan to determine genetic relationships among native, captive, and naturalized populations in the upper Missouri River basin, to assess patterns in genetic diversity, and to infer recent demographic histories. Substantial genetic subdivision was observed among sample populations (global FST= 0.10). Canadian populations have been isolated from Missouri River populations long enough for mutation to cause genetic differences between regions (mean pairwise FST= 0.18, RST= 0.54). Within the Missouri River basin, most naturalized lacustrine populations traced their ancestry to Red Rock lakes. Two populations in headwater lakes within the Big Hole River watershed appear to be native. We found neither evidence for introgression of Canadian-origin grayling nor any effect of hatchery stocking in native populations. The native fluvial Big Hole River group was genetically distinct and most diverse (HE= 0.89), whereas native Madison River and Red Rock lakes populations exhibited lower genetic diversity (HE= 0.74 and 0.80, respectively) and evidence of recent bottlenecks. The existing Big Hole and Red Rock populations are at low abundance but do not appear to be at immediate risk of inbreeding depression (Ne= 207.7–228.2).
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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".