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Record W4210931427 · doi:10.1002/ecs2.3918

The socioeconomic status of cities covaries with avian life‐history strategies

2022· article· en· W4210931427 on OpenAlexafffund
Riikka Kinnunen, Kevin C. Fraser, Chloé Schmidt, Colin J. Garroway

Bibliographic record

VenueEcosphere · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsSocioeconomic statusPasserineGeographyUrban sprawlUrbanizationEcologyEcosystemUrban ecosystemBiologyUrban planningDemographyPopulation

Abstract

fetched live from OpenAlex

Abstract Cities are the planet's newest ecosystem and thus provide the opportunity to study community formation directly following major permanent environmental change. The human social and built components of environments can vary widely in different cities, yet it is largely unknown how features of cities covary with the traits of colonizing species despite humans being the ultimate cause of environments and disturbances in cities. We constructed a dataset from open‐source data comprised of 13,502 breeding season observations of 213 passerine species observed in 551 Census‐defined urban areas across the United States. We found that as a city became more compact with less sprawl it tended to support more migratory species and species with lower body mass, shorter lifespans, and larger clutches. We also found that species had lower body mass in cities with higher median income, and higher body mass in highly populated cities. Our results highlight the complexity of human‐dominated urban ecosystems, where human socioeconomic actions and everyday activities intermix leading to structurally heterogeneous environments that support the colonization of some species over others.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.973

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0280.000

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.

Opus teacher head0.008
GPT teacher head0.181
Teacher spread0.173 · 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 teacher head, not a consensus.

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

Citations10
Published2022
Admission routes2
Has abstractyes

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