Investigating the Role of Management Decisions in Subspecies Hybridization Across the Wild Turkey’s Range
Bibliographic record
Abstract
ABSTRACT The expanded geographic range and recovery to millions of wild turkeys across the country would not have been possible without management actions that included introducing and translocating individuals. However, the range-wide genetic impact of management decisions on one of North America’s greatest conservation success stories remains unknown despite the potential economic impact as hunters seek out easily identifiable subspecies for grand slams. In this study, we used DNA extracted from hunter-collected feathers from 29 states and Ontario to investigate genetic differences among turkeys in their historic and introduced ranges. Additionally, we compiled state-level management data to investigate how different management decisions are associated with the amount of admixture among subspecies. We found no difference in the amount of admixture in the turkey’s historic range compared to the introduced range. However, management decisions like as the number of subspecies introduced and the number of unique source states resulted in an increased level of admixture detected, but there was no relationship in admixture and the number of unique relocated counties. This first investigation into the hybridization among subspecies of wild turkey provides evidence that individual state’s management actions have influenced the genetic makeup of subspecies in that state.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".