Predation on Artificial Nests of Northern Bobwhites, <em>Colinus virginianus</em>, by Mammalian Mesopredators: Does the Problem-Individual Paradigm Fit?
Bibliographic record
Abstract
Using mark/recapture procedures, predation on artificial nests of Northern Bobwhites (Colinus virginianus) by mammalian mesopredators (Raccoons, Procyon lotor; Virginia Opossums, Didelphis virginiana; and Striped Skunks, Mephitis mephitis) was assessed in relation to the “problem-individual” paradigm. The paradigm, which is untested among mammalian mesopredators, predicts that most predation on a prey species is by a small number of individuals repeatedly involved. By examining number of captures and recaptures on artificial nests during non-nesting and nesting periods in 2000-2003, predation within and among species were gauged. Results varied by species, sampling period, and year; however, predation was by a small percentage of individuals and only within the population of Striped Skunks were individuals (2 of 49) captured on an artificial nest more than once. Raccoons, Virginia Opossums, and Striped Skunks were responsible for 10, 2, and 12% of the loss of all nests available to predators, respectively. Based on low occurrences of individuals repeatedly involved in predation on nests, the problem-individual paradigm was unsubstantiated. Although Raccoons, Virginia Opossums, and Striped Skunks (as individual predators) had only moderate impact on the population of Northern Bobwhites, they had a much greater impact collectively (preying upon 24% of all nests available). To maximize Northern Bobwhite success, the most productive management is probably best directed toward a vertebrate guild that includes mammalian, avian, and reptilian predators, and management strategies that use multiple techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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".