Ecology and management of raccoons within an intensively managed forest in the central Appalachians
Bibliographic record
Abstract
The raccoon (Procyon lotor), a generalist meso-predator, is commonly found throughout the eastern United States. Many researchers have examined the ecology and spatial requirements of raccoons in agricultural and wetland areas of the mid-western and southeastern United States. However, no studies have quantitatively examined raccoon habits in the forested central Appalachians and their response to forest management. During the fall of 2000 through the spring of 2003, I monitored the spatial movements and den site selection of raccoons within an intensively managed forest.;I investigated the occurrence of raccoon roundworm (Baylisascaris procyonis) in raccoons (Procyon lotor) within my study area. I found no evidence of B. procyonis infection in 25 raccoons sampled by fecal floatation and necropsy methodologies. On the basis of my 25 negative cases at a 95% confidence level the estimated non-detectable maximum constant prevalence rate is 8%. Baylisascaris procyonis has been implicated in population declines of the Allegheny woodrat (Neotoma magister) in the northeastern United States. The low prevalence of B. procyonis in an area inhabited by what is believed to be a stable population of Allegheny woodrats supports conservation measures to monitor anthropogenic activities that may increase the prevalence of B. procyonis or raccoon interaction with Allegheny woodrats.;During my study, I found 13% of all active raccoon locations to be below 800 m elevation and 55% and 92% of the active locations below 900 m and 1000 m respectively. According to the stream buffer analysis, I found 63% of all active locations to be within 200 m of a steam and 82%, 92%, and 98% of all active locations to be within 300 m, 400 m, and 500 m respectively. Restructuring the rabies vaccination bait-drop area on my study site to include an elevational ceiling of 1000 m and focusing the drop zone to within 400 m on either stream bank to target 92% of the nocturnal activity, would effectively reduce the bait-drop area by 36%, while maintaining >70% contact with all animals. Applying these same parameters of 1000 m elevation and within 400 m of a stream would reduce the bait drop area at the county (Randolph) level by 22%. (Abstract shortened by UMI.).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".