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Record W7064147902

An Application of Autism-responsive Landscape Design in Commercial Plaza Space

2023· dissertation· en· W7064147902 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Atrium (University of Guelph) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLandscape designUrban landscapeSpace (punctuation)Reflection (computer programming)Landscape planningUrban planningEnvironmental designLevel design
DOInot available

Abstract

fetched live from OpenAlex

Contemporary urban landscape design, prioritizes a generalized population, overlooking families and children with autism. Outdoor commercial landscapes in particular are regularly visited but fail to account for the diverse needs of visitors. The aim of this study is to explore and evaluate landscape design strategies for outdoor commercial landscapes that minimize environmental barriers and support diverse needs of children with autism and their families. Following an analysis and synthesis of autism-focused design guideline, a case study of a commercial plaza in Guelph, Ontario was used to assess the design guideline application. Findings showed that there are several issues that need to be addressed in an outdoor commercial environment, including safety risks, sensory stimulation, and lack of legible environment. Finally, this study provides critical reflection for landscape and urban designers to improve outdoor commercial environments in the future to make them more suitable for families and children with autism.

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)0.000
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 score0.743
Threshold uncertainty score0.441

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)0.0000.000

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 head0.016
GPT teacher head0.242
Teacher spread0.226 · 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