Influence of habitat attributes on density of Virginia opossums (<i>Didelphis</i><i> virginiana</i>) in agricultural ecosystems
Bibliographic record
Abstract
In agriculturally fragmented ecosystems, mesopredators play dominant roles in food webs through scavenging. We examined the influence of habitat attributes associated with carrion on local Virginia opossum (Didelphis virginiana Kerr, 1792) density in an agricultural landscape. We conducted opossum mark–recapture in 25 forest patches from 2005 to 2010, which represented the most extensive sampling of opossums to date. We analyzed mark–recapture data with a closed robust design and evaluated effects of landscape features linked to carrion on opossum density and female opossum density with generalized linear mixed-effects models. We included landscape-level (1481.6 m buffer) and patch-level covariates linked to carrion in addition to other covariates associated with high opossum densities. We developed a set of 19 candidate models and examined model fit with Akaike’s information criterion. The top model for opossum density included the density of adjoining roads, whereas the top model for female density included patch size, although the statistical null was a competing model in both cases. The long-distance dispersal capability and generalist diet of the opossum likely precluded us from detecting a definitive relationship between covariates and opossum density. The scale of effect for opossum density in agriculturally fragmented landscapes is likely larger than the spatial scales examined here.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".