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Record W4243728930 · doi:10.32920/ryerson.14663529

Re-Designing Contemporary City Blocks: Designing in Favour of Energy Conservation for a City in Desert: Kerman, Iran

2021· preprint· en· W4243728930 on OpenAlexaff
Paria Sajadpour

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsArchitectural engineeringArchitectureUrban designNeighbourhood (mathematics)Energy conservationEnergy consumptionScale (ratio)Efficient energy useThermal comfortBlock (permutation group theory)Architectural designDesert (philosophy)AridCivil engineeringEnvironmental planningEnvironmental resource managementGeographyEnvironmental scienceEngineeringPolitical scienceEcologyCartography

Abstract

fetched live from OpenAlex

In Iran, urban block morphology has changed as a result of the architectural focus shifting away from traditional concerns such as climate-and-energy sensitivity onto issues such as land use, transportation and finance. Current architectural practice while has completely overlooked the architecture of the past, failed to improve the quality of life. The hot, arid climate in combination with non-responsive urban building design has resulted in high energy consumption to keep occupants comfortable. Although it is possible to overcome many of the negative effects of an inefficient design by the use of mechanical systems, this thesis through an architectural response, explores the role of climate sensitive strategies, practiced in the traditional architecture, in recognizing the importance of energy conservation. While it is only at the urban scale that energy-saving strategies could effectively tackle problems, the applicability of these principles will be studied at a neighbourhood scale.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.003
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.154
GPT teacher head0.286
Teacher spread0.132 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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