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Record W2612699220

Balancing Perceived Sensory Dimensions and Biotopes in Urban Green Space Design

2017· dissertation· en· W2612699220 on OpenAlexaboutno aff
Amanda Lockwood

Bibliographic record

VenueThe Atrium (University of Guelph) · 2017
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsBiotopeSpace (punctuation)Sensory systemArchitectural engineeringUrban designUrban green spaceGeographyAestheticsComputer sciencePsychologyCognitive psychologyEngineeringEcologyArtArchitectureArchaeologyBiologyHabitat
DOInot available

Abstract

fetched live from OpenAlex

Eight Perceived Sensory Dimensions (PSDs) were identified from previous studies to describe user preferences of park qualities and characteristics: nature, culture, prospect, social, space, rich-in-species, refuge, and serene. Recently, PSDs and biotopes have been integrated to enhance park users’ preferences and vegetation structure. Usable green space needs to balance social aspects (PSDs) and environmental aspects (biotopes) at the design stage. This study assesses urban green spaces through experimental design based on the inclusion of the biotope ‘green space’ and PSDs. Designs were created based on market squares in Guelph and London, Ontario, by including biotope characteristics for plazas and PSDs. Designs were critically analyzed to determine that PSDs and the biotope category ‘plaza’ had a positive relationship aside from the PSD ‘nature’. This research contributes to the understanding of socially and environmentally cohesive urban green spaces, providing landscape architects with tools for creating usable green spaces in Southern Ontario 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.953

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.231
Teacher spread0.209 · 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.

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

Citations6
Published2017
Admission routes1
Has abstractyes

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