Population genetic structure of North American thinhorn sheep (<i>Ovis dalli</i>)
Bibliographic record
Abstract
The thinhorn sheep (Ovis dalli ssp.) provides a rare example of a North American large mammal that occupies most of its native range and maintains close to ancestral population size. There are currently two recognized subspecies, Dall's sheep (O. d. dalli) and Stone's sheep (O. d. stonei), the validity of which remains uncertain. We investigated the spatial genetic structure of thinhorn sheep populations representing both subspecies by genotyping individuals (n = 919) from across the species range at 12 variable microsatellite loci. We found high levels of genetic diversity within (HE = 0.722) and significant genetic structure among the 24 sampled areas (FST = 0.160). Genetic distance measures and Bayesian clustering analyses revealed the presence of at least eight subpopulations that are delineated by mountain range topology. A strong overall pattern of isolation-by-distance is evident across the sampling range (r = 0.75, P < 0.001) suggesting limited dispersal and extensive philopatry. Partial Mantel tests of this relationship showed mountain range distinctions represent significant barriers to gene flow (P = 0.0001), supporting the Bayesian analyses. Genetic structure was more strongly pronounced in southern Yukon and Alaska than elsewhere. We also show evidence for genetic differences between the two currently recognized thinhorn subspecies.
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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) | 0.000 | 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".