Influence of sampling scheme on the inference of sex-biased gene flow in the American badger (<i>Taxidea taxus</i>)
Bibliographic record
Abstract
Population genetics has fueled a substantial growth in studies of dispersal, a life-history trait that has important applications in ecology and evolution. Mammals typically exhibit male-biased gene flow, so this pattern often serves as a null hypothesis in empirical studies. Estimation of dispersal using population genetics is not without biases, so we utilized a combination of population genetic methods and simulations to evaluate gene flow within the American badger ( Taxidea taxus (Schreber, 1777)), a highly elusive and poorly understood mustelid. A total of 132 badgers captured between 2001 and 2002 were genotyped at nine microsatellite loci to investigate fine-scale genetic structure consistent with philopatry in females and dispersal in males. Resultant genetic patterns were largely consistent with a panmictic population with little evidence for sex-biased dispersal, and simulations confirmed that our sampling scheme did not substantially impact our statistics. An overall deficiency of heterozygotes was observed across the Lower Peninsula, which indicates either a Wahlund effect, mixing of separate populations, or inbreeding. Our study emphasizes the importance in deciphering between actual behavioral mechanisms and sampling effects when interpreting genetic data to understand other factors that influence dispersal like population density and territoriality.
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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 | 0.039 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".