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Record W562493868

An exploration of thermal comfort and environmental perception in support of ecologically designed human habitat

2009· book· en· W562493868 on OpenAlexaboutno aff
Maxim Erik Coleman

Bibliographic record

VenueThe Atrium (University of Guelph) · 2009
Typebook
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatPerceptionGeographyEnvironmental resource managementEnvironmental scienceEnvironmental planningEcologyPsychologyBiologyNeuroscience
DOInot available

Abstract

fetched live from OpenAlex

A sustainable future requires communities that are socially active. Ecological design incorporates natural processes into urban areas and is an opportunity to provide comfortable, restorative environments that can promote social interaction. With this in mind, this research looks for a relationship between site aesthetic qualities and human thermal comfort on several ecologically-functional, multi-use trails in Guelph, Ontario, Canada. Environmental perceptions were assessed using Kaplan, Kaplan and Ryan's (1998) environmental preference patterns and human thermal comfort was assessed using the COMFA model (1995). Environmental preference ratings were highest for areas with canopy coverage, and lowest for open park spaces. Both environmental preference ratings and human thermal comfort levels were influenced by vegetation but in different ways. This exploratory research suggests that ecological design, based on environmental preferences and thermal comfort, can benefit both people and the environment directly.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.015
GPT teacher head0.192
Teacher spread0.176 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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