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Record W4408982021 · doi:10.18280/acsm.490104

The effect of Changing Construction Materials on Historical Building Performance in Case of Restoration - Case Study of Al-Nabi Jirjis Mosque in Old Mosul City

2025· article· en· W4408982021 on OpenAlexvenueno aff
Khalid Ismail, Faris Ataallah Matloob, Hafedh Abed Yahya

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsnot available
FundersUniversity of Mosul
KeywordsArchitectural engineeringCivil engineeringConstruction engineeringEngineeringForensic engineeringGeography

Abstract

fetched live from OpenAlex

Choosing appropriate construction materials in restoration processes of historical buildings particularly after wars or disasters is crucial in retaining the importance of such buildings and their distinct characteristics.This study is to explore how a change of original construction materials can influence energy efficiency and thermal comfort when rebuilding the destroyed historic buildings after the war in Old Mosul, where Al-Nabi Jrjis Mosque was selected as a case study.The study has adopted a comparative method between three simulation scenarios including two sets of new materials in addition to the original one.ENVI met analysis is used as a method to simulate the three cases.Results showed that the original set of materials (stone and plaster) has characterized by the best thermal efficiency compared by new materials used in the study.However, a set of hollow concrete with PVC strip used in one of the other scenarios revealed somehow good results, which therefore can be used as an acceptable alternative if the original materials are not available.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

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

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 head0.035
GPT teacher head0.297
Teacher spread0.262 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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