Genetic structure of the arboreal squirrels (<i>Glaucomys sabrinus</i>and<i>Tamiasciurus hudsonicus</i>) in the North American Black Hills
Bibliographic record
Abstract
We selected two isolated mammalian populations, the Black Hills northern flying squirrel ( Glaucomys sabrinus (Shaw, 1801)) and red squirrel ( Tamiasciurus hudsonicus (Erxleben, 1777)), to elucidate their genetic structure. We trapped both squirrels from 2005 to 2007, in three regions of the Black Hills, differing in geology and vegetation, to collect ear samples for genetic analyses. Microsatellite loci (northern flying (9) and red squirrel (13)) were used to examine genetic structure. Data analyses estimated genetic variability, substructure, and gene flow. Northern flying and red squirrel populations have allelic diversity and observed heterozygosity similar to other isolated populations. Each species shows weak substructure from STRUCTURE and GENELAND analyses, suggesting squirrel movements may be inhibited by topography or unsuitable habitat. Recent gene flow estimates from BAYESASS indicate that both species experience some within population gene flow and red squirrels may be more structured than northern flying squirrels because of lower migration rates. Concordant patterns of genetic structure in northern flying and red squirrels indicate that other species’ movements in the Black Hills may be affected by topography and habitat. Because their habitat is isolated in the Black Hills, management practices and conservation measures are recommended to promote viability and survival of each species.
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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) | 0.001 | 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".