Habitat selection by a generalist mesopredator near its historical range boundary
Bibliographic record
Abstract
The Virginia opossum (Didelphis virginiana Kerr, 1792) has expanded its geographic range northward since European settlement, which has been attributed to its ability to exploit anthropogenic resources. To examine the utility of anthropogenic resources to this species, we monitored 61 opossums from 2009 to 2010 with very high frequency (VHF) telemetry in a fragmented agricultural ecosystem in northern Indiana, USA, at the periphery of the opossum’s historical distribution. We examined the influence of anthropogenic (agricultural areas, developed land, roads), disturbed (corridor, forest edge, grassland, water), and native (forest, shrub land) habitats on habitat selection at the second- and third-order scales across three seasons. At the second-order scale, areas proximate to agricultural fields and developed land were selected in the breeding and postbreeding seasons, respectively. Areas proximate to roads were selected at both spatial scales during all seasons except winter at the third-order scale. Areas near forest with high forest-edge density were selected throughout the year at both spatial scales, but confidence intervals for forest during the postbreeding season marginally overlapped zero (third-order scale). Although anthropogenic habitats provide novel resources for opossums, forest and forest edge remain essential components to populations near their historical distributional limit in agricultural ecosystems.
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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 | 0.000 |
| 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".