Contemporary range expansion of the Virginia opossum (<i>Didelphis</i> <i>virginiana</i>) impacted by humans and snow cover
Bibliographic record
Abstract
Range expansions are key demographic events driven by factors such as climate change and human intervention that ultimately influence the genetic composition of peripheral populations. The expansion of the Virginia opossum (Didelphis virginiana Kerr, 1792) into Michigan has been documented over the past 200 years, indicating relatively new colonizations in northern Michigan. Although most contemporary expansions are a result of shifts in climate regimes, the opossum has spread beyond its hypothesized climate niche, offering an opportunity to examine the compounding influence that climate change and humans have on a species’ distribution. The genetic consequences of two range expansions were investigated using genotypic data for nine microsatellite markers from opossums collected in Michigan, Ohio, and Wisconsin, USA. Two genetic clusters were identified: one on either side of Lake Michigan. Using general linear models, we found that measurements of genetic diversity across 15 counties are best explained by days of snow on the ground. Next best models incorporate anthropogenic covariates including farm density. These models suggest that opossum expansion may be facilitated by agricultural land development and at the same time be limited by their inability to forage in snow.
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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 | 0.000 | 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".