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Record W2468222642 · doi:10.1139/er-2016-0029

Sublethal consequences of urban life for wild vertebrates

2016· article· en· W2468222642 on OpenAlexafffundvenue
Kim Birnie‐Gauvin, Kathryn S. Peiman, Austin J. Gallagher, Robert de Bruijn, Steven J. Cooke

Bibliographic record

VenueEnvironmental Reviews · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Fish and Wildlife ServiceCanada Research Chairs
KeywordsUrbanizationWildlifeEcologyHabitatBiodiversityContext (archaeology)BiologyUrban ecologyUrban ecosystemEcosystemGeographyWildlife conservation

Abstract

fetched live from OpenAlex

Urbanization is modifying previously pristine natural habitats and creating “new” ecosystems for wildlife. As a result, some animals now use habitat fragments or have colonized urban areas. Such animals are exposed to novel stimuli that they have not been exposed to in their evolutionary history. Some species have adapted to the challenges they face — a phenomenon known as synurbanization — while others have not. Here we present a review of the sublethal consequences of life in the city for wild vertebrates, and demonstrate that urban animals face an almost completely different set of physiological and behavioural challenges compared to their rural counterparts. We focus on the negative fitness-related impacts of urbanization, but also identify instances where there are benefits to wildlife. The effects of urbanization appear to be both species- and context-dependent, suggesting that although the field of urban ecology is far from nascent, we are still just beginning to understand how the intricacies of biodiversity on our planet are affected by our presence.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.233
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

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

Citations89
Published2016
Admission routes3
Has abstractyes

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