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Record W592965421

Raccoons' intrusion into urban dwellings: GIS application on urban wildlife study

2015· article· en· W592965421 on OpenAlexaboutno aff
Xiaotian Wang

Bibliographic record

VenueYork University Digital Library (York University) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeIntrusionGeographyEnvironmental planningEcologyGeologyBiology
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.010
GPT teacher head0.165
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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