Raccoons' intrusion into urban dwellings: GIS application on urban wildlife study
Bibliographic record
Abstract
The history of raccoons entering urban life of human can go back to the beginning of the 20th century (Lariviere, 2004; Bateman & Fleming, 2012). While some people see this animals as rewarding wilderness encounter, others may considers them as threatening safety concerns. (Clark, 1994) Indeed, the discussion around these highly adaptive creatures living in the cities has been going on for decades. A study in Texas, US shows that since 1980s, raccoons have been recognized as the second largest cause of complaints regarding human-wildlife conflict, after rats and mice (Chamberlain et al., 1981). Raccoons are rabies-vector mammals, and also carry at least 13 other pathogens which are potential threats to human’s health (Lotze & Anderson, 1979; Wolch, 1995; Bateman & Fleming, 2012). Furthermore, there are evidences showing that driving by anthropogenic food sources and shelter, raccoons not only wander in the yards and raid garbage cans, but also settle down in houses as their den sites (Bateman & Fleming, 2012; Prange et al., 2003). They invaded through anywhere they could fit, such as roofs, chimneys, vents and even underneath the porches (Wolch et al., 1995; Clark, 1994). The facts that raccoons carry diseases around and cause destruction to the buildings brought urban residents to professional wildlife management organizations for help. \n \nOn the other hand, the encounter of raccoons to urban people seems unavoidable. Raccoons living in the urban cities are considered to have better physical conditions and therefore higher survival rates, compared to their rural neighbours (Prange et al., 2003; Bateman & Fleming, 2012). Their major predators in the cities are cars, which is the number one cause of death according to Bateman and Fleming’s investigation (2012). Some scholars believe that raccoons tend to avoid roads and build-up areas (Bateman & Fleming, 2012), while other researchers, such as Ditchkoff and her colleagues (2006), suggested that raccoons forage on road-killed animals, which indicates their presence alongside the roads. Overall as natural creatures, raccoons have favor in parks and green spaces in the cities (Bateman & Fleming, 2012). It is worthy to notice that in many new suburban areas, larger areas with trees and other vegetation are preserved to separate the houses, which provides perfect wildlife habitats (travel, forage, cover etc.) for the animals (Ditchkoff et al., 2006). \n \nThis paper investigates the spatial pattern of raccoons’ intrusion to dwellings in Toronto, Canada, in terms of which part of house they were found. Raccoons are highly adaptive mammals living in the urban settings, therefore it is possible to assume that the animals living nearby or having overlapped home range may learn from each other, which may be reflected by their den choices. A spatial illustration could help us learn more about raccoons’ behavior and adaptation to new environment, which is important to urban wildlife management practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".