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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 OpenAlexaff
Mojtaba Parsaee, Tarlan Abazari, Soroush Samareh Abolhassani

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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

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 designSimulation or modeling
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

Citations3
Published2022
Admission routes1
Has abstractyes

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