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Record W2794263711 · doi:10.1002/ecs2.2105

Homogenization of plant diversity, composition, and structure in North American urban yards

2018· article· en· W2794263711 on OpenAlexaff
William D. Pearse, Jeannine Cavender‐Bares, Sarah E. Hobbie, Meghan L. Avolio, Neil D. Bettez, Rinku Roy Chowdhury, Lindsay Darling, Peter M. Groffman, J. Morgan Grove, Sharon J. Hall, James B. Heffernan, Jennifer Learned, Christopher Neill, Kristen C. Nelson, Diane E. Pataki, Benjamin L. Ruddell, Meredith Steele, Tara L. E. Trammell

Bibliographic record

VenueEcosphere · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
FundersDivision of Emerging FrontiersDirectorate for Biological SciencesNational Science Foundation
KeywordsHomogenization (climate)YardBiodiversityUrban ecologyEcosystemGeographyEcologyHomogeneousUrban ecosystemBiologyUrban planningUrbanization

Abstract

fetched live from OpenAlex

Abstract Urban ecosystems are widely hypothesized to be more ecologically homogeneous than natural ecosystems. We argue that urban plant communities assemble from a complex mix of horticultural and regional species pools, and evaluate the homogenization hypothesis by comparing cultivated and spontaneously occurring urban vegetation to natural area vegetation across seven major U.S. cities. There was limited support for homogenization of urbandiversity, as the cultivated and spontaneous yard flora had greater numbers of species than natural areas, and cultivated phylogenetic diversity was also greater. However, urban yards showed evidence of homogenization ofcompositionandstructure. Yards were compositionally more similar across regions than were natural areas, and tree density was less variable in yards than in comparable natural areas. This homogenization of biodiversity likely reflects similar horticultural source pools, homeowner preferences, and management practices across U.S. cities.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.967
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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

Citations121
Published2018
Admission routes1
Has abstractyes

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