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Record W2765886301 · doi:10.1126/science.aam8327

Evolution of life in urban environments

2017· review· en· W2765886301 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsAmorfix (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsGeography

Abstract

fetched live from OpenAlex

Our planet is an increasingly urbanized landscape, with over half of the human population residing in cities. Despite advances in urban ecology, we do not adequately understand how urbanization affects the evolution of organisms, nor how this evolution may affect ecosystems and human health. Here, we review evidence for the effects of urbanization on the evolution of microbes, plants, and animals that inhabit cities. Urbanization affects adaptive and nonadaptive evolutionary processes that shape the genetic diversity within and between populations. Rapid adaptation has facilitated the success of some native species in urban areas, but it has also allowed human pests and disease to spread more rapidly. The nascent field of urban evolution brings together efforts to understand evolution in response to environmental change while developing new hypotheses concerning adaptation to urban infrastructure and human socioeconomic activity. The next generation of research on urban evolution will provide critical insight into the importance of evolution for sustainable interactions between humans and our city environments.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.071
GPT teacher head0.337
Teacher spread0.266 · 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