Extent and direction of introgressive hybridization of mule and white‐tailed deer in western Canada
Bibliographic record
Abstract
) appears to be a semi-regular occurrence in western North America. Previous studies confirmed the presence of hybrids in a variety of sympatric habitats, but their developing molecular resources limited identification to the earliest, most admixed generations. For this reason, estimates of hybrid production in wild populations often rely on anecdotal reports. As well, white-tailed deer populations' continued encroachment into historically mule deer-occupied habitats due to changes in land use, habitat homogenization, and a warming climate may increase opportunities for interspecific encounters. We sought to quantify the prevalence and extent of hybrid deer in the prairies of western Canada using a SNP assay with enhanced discriminating power. By updating the available molecular resources, we sought to identify and characterize previously cryptic introgression. We also investigated the influence of various parameters on hybridity by way of logistic regression. We observed overall hybridization rates of ~1.0%, slightly lower than that reported by previous studies, and found white-tailed-like hybrids to be more common than their mule deer-like counterparts. Here, we build upon past studies of hybridization in North American deer by increasing hybrid detection power, expanding sample sizes, demonstrating a new molecular resource applicable to future research and observing asymmetrical directionality of introgression.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".