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Record W4414998924 · doi:10.1016/j.ufug.2025.129110

Lessons from exploring the relationship between livability and biodiversity in the built environment

2025· article· en· W4414998924 on OpenAlexafffund
Morteza Hazbei, Tatev Yesayan, Nicole Yu, Kayleigh Hutt‐Taylor, Carly D. Ziter

Bibliographic record

VenueUrban forestry & urban greening · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsBiodiversityEcosystem servicesBuilt environmentDiversity (politics)SustainabilityEcosystemQuality (philosophy)Discipline

Abstract

fetched live from OpenAlex

Urban design and ecology usually operate in silos despite their shared goal of advancing sustainable cities. Livability is predominantly concerned with the quality of the built environment, often neglecting ecological considerations. Conversely, biodiversity focuses on preserving life forms and ecosystem functions, often overlooking the intricacies of the built environment. This disjointed approach limits our understanding of the interactions between these fields and hinders interdisciplinary practice. Our research explores the existing relationships between biodiversity and livability to expand our understanding of the exchanges between the two disciplines, and their combined effect in improving our cities. To achieve this, we employed an expert elicitation method grounded in a heuristic, exploratory, and knowledge-based approach. This methodology enabled us to first deconstruct the broad and often ambiguous concepts of livability and biodiversity into concrete domains, each with its own specific components, and then examine the connections between the two fields in the urban environment. The results of this study reveal that although urban designers and ecologists approach the concepts of livability and biodiversity through strict disciplinary lenses, the two share significant areas of interaction. Our findings indicate that biodiversity acts as a hidden driver of livability. Functional Diversity emerges as a key domain of biodiversity, impacting domains of livability such as Comfort, and Sense of Place. Conversely, specific livability components such as Environmental Hazards Mitigation, Maintenance, Civic and Social Involvement, and Infrastructure Accessibility have the biggest impact on biodiversity across all domains. • Urban biodiversity is a hidden driver of livability. • Sense of place, comfort, and safety are closely tied to key biodiversity components. • Species diversity and landscape composition are tied to livability components. • Achieving sustainable cities requires a cross-disciplinary approach. Biodiversity and urban design interlink in complex ways, more collaboration needed.

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)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0030.019
Scholarly communication0.0070.013
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.098
GPT teacher head0.276
Teacher spread0.179 · 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 designQualitative
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

Citations5
Published2025
Admission routes2
Has abstractyes

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