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Record W2097336647 · doi:10.5539/jsd.v4n1p264

An Overview of Some Vernacular Techniques in Iranian Sustainable Architecture in Reference to Cisterns and Ice Houses

2011· article· en· W2097336647 on OpenAlexvenueno aff
Amir Ghayour Kazemi, Amir Hossein Shirvani

Bibliographic record

VenueJournal of Sustainable Development · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsnot available
Fundersnot available
KeywordsVernacularVernacular architectureArchitectureNatural (archaeology)Architectural engineeringWork (physics)Natural resourceAestheticsHistoryPolitical scienceArchaeologyLiteratureArtEngineeringLaw

Abstract

fetched live from OpenAlex

The great Iranian tradition is as yet little known in the West and there is much to be learnt both from it and the building techniques which are integral with it. Meanwhile, Not only is the Iranian vernacular building tradition itself still alive, but there is much to be gained from the knowledge of a highly developed technology which makes such ingenious use of natural resources without the consumption of additional power. This article is a study of the craftsmanship involved in the construction of the mud brick vernacular architecture of Iran, and the cultural aspects of a traditional architecture which incorporates an understanding of buildings which dates back centuries. Expanding the existing knowledge of these earthen heritage properties, examining their behaviour in the local climate and explaining their current condition in order to express the need for the preservation of traditional craftsmanship as part of a sustainable conservation future are the other prominent concerns of this work. Among different Iranian Vernacular constructions, Ice-houses and cisterns are the subject of the main body of this article.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.003
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.066
GPT teacher head0.279
Teacher spread0.213 · 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
GenreReview

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

Citations8
Published2011
Admission routes1
Has abstractyes

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