Influence of landscape attributes on Virginia opossum density
Bibliographic record
Abstract
Abstract The Virginia opossum (Didelphis virginiana), North America's only marsupial, has a range extending from southern Ontario, Canada, to the Yucatan Peninsula, Mexico, and from the Atlantic seaboard to the Pacific. Despite the Virginia opossum's taxonomic uniqueness in relation to other mammals in North America and rapidly expanding distribution, its ecology remains relatively understudied. Our poor understanding of the ecology of this important mesopredator is especially pronounced in the rural southeastern United States. Our goal was to estimate effects of habitat on opossum density within an extensive multi‐year spatial capture‐recapture study. Additionally, we compared the results of this spatial capture‐recapture analysis with a simple relative abundance index. Opossum densities in the relatively underdeveloped regions of the southeastern United States were lower compared to the more human‐dominated landscapes of the Northeast and Midwest. In the southeastern United States, Virginia opossums occurred at a higher density in bottomland swamp and riparian hardwood forest compared to upland pine (Pinus spp.) plantations and isolated wetlands. These results reinforce the notion that the Virginia opossum is commonly associated with land cover types adjacent to permanent water (bottomland swamps, riparian hardwood). The relatively low density of opossums at isolated wetland sites suggests that the large spatial scale of selection demonstrated by opossums gives the species access to preferable cover types within the same landscape.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".