Demographic responses of Virginia opossums to limitation at their northern boundary
Bibliographic record
Abstract
The precise response of a population at its distributional edge to the limiting extrinsic factor should be mediated by the demography of the species. We applied this principle to understanding the northern distributional potential of the Virginia opossum (Didelphis virginiana Kerr, 1792). We reviewed the literature for demographic data that we then used to build model populations. Juvenile over-winter survival was adjusted to determine the survival necessary for a stable population. To put the results in the context of life-history strategy and ecological niche, we built models for two other medium-sized mammals with similar distributions, the raccoon (Procyon lotor (L., 1758)) and the muskrat (Ondatra zibethicus (L., 1766)). Northern raccoon populations may sustain juvenile winter survival rates of <0.50 because adult females live to reproduce in multiple years. Muskrat juveniles may need a winter survival rate of only 0.40 in average years because reproduction is very high. In contrast, young northern opossums need a survival rate of 0.81 over winter to compensate for low prewinter survival. Raccoons and muskrats, through different life-history strategies, should be able to expand their northern distribution to the winter-induced physiological limit. However, opossum populations should fail before the average individual physiological limit is reached.
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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".