Data from: Fine-scale genetic structure and conservation status of American badgers at their northwestern range periphery
Bibliographic record
Abstract
Peripheral populations are often characterized by small population size and low genetic diversity, with many at risk of extirpation. These characteristics may be even more pronounced in human-modified landscapes that further reduce the resiliency of populations to environmental change. Situated at the northwestern edge of the species’ range, the western American badger (Taxidea taxus jeffersonii) is an endangered mammal in Canada, where it inhabits the interior grassland and open forest ecosystems of British Columbia (BC) and continues to be threatened by severe vehicle-induced mortality rates and other anthropogenic factors. Here, we collected mitochondrial DNA haplotypic and microsatellite genotypic data to investigate the extent and distribution of American badger genetic variation within and among sites in British Columbia, and relative to adjacent populations in the USA, including in Washington state. From these data, we reconstructed population structure and connectivity, and examined current designatable unit status. Patterns of genetic variation for American badgers in British Columbia were as expected for peripheral populations, including reduced genetic diversity, increased population differentiation, and evidence of demographic contraction. Furthermore, we found limited connectivity between regional populations in our study area and identified significant substructure isolating the most northwestern sampling unit (Cariboo), findings that starkly contrast with the high levels of gene flow observed between populations across the species’ range core. These results have important implications for current designatable unit status for western American badgers in British Columbia and emphasize the need for further population monitoring and mitigation of potential barriers to gene flow.
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".