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Record W4293175763 · doi:10.1080/00207233.2022.2117479

Natural ventilation in a traditional city: an exploratory computational study of Bushehr-Iran

2022· article· en· W4293175763 on OpenAlex
Mojtaba Parsaee, Tarlan Abazari, Soroush Samareh Abolhassani

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.

Bibliographic record

VenueInternational Journal of Environmental Studies · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsConcordia UniversityMcGill UniversityUniversité Laval
Fundersnot available
KeywordsAirflowNatural ventilationVentilation (architecture)Computational simulationNatural (archaeology)Environmental scienceComputational fluid dynamicsWork (physics)Architectural engineeringMeteorologyComputer scienceEngineeringGeographyMechanical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

This exploratory research discusses natural ventilation in a traditional city through employing a computational method. The mechanism of airflow in traditional cities has not yet been sufficiently studied through experimental and computational methods. This paper reports work on simulating natural ventilation in traditional Bushehr City by means of computational models of airflow in the study zone under three design scenarios. Simulations show that the unstructured plan of the city is less likely to improve air circulation and velocity ratios. But the main corridor with a relatively wider width and straight direction is more likely to contribute to urban ventilation. Further use of evidence-based methods to test climate-responsive strategies of traditional cities may be helpful.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
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 head0.055
GPT teacher head0.276
Teacher spread0.221 · 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