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Record W1519032648 · doi:10.3846/13928619.2007.9637780

MANAGERIAL AND ECONOMIC OPTIMISATIONS FOR PREFABRICATED BUILDING SYSTEMS

2007· article· en· W1519032648 on OpenAlexaff
Ahmed Abdallah

Bibliographic record

VenueTechnological and Economic Development of Economy · 2007
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsSimple (philosophy)Point (geometry)Architectural engineeringComputer scienceRisk analysis (engineering)Environmental economicsBusinessEconomicsEngineeringMathematics

Abstract

fetched live from OpenAlex

This paper deals with new realisations of research, scientific development, managerial and economic optimisations – a prefabricated building systems. In the literature, all scientific fields are worked out based on efficiencies, costs, benefits, choice of execution design, technical demands, aesthetical point of view, as well as the total economics. In the present example of a school building for communities in developing countries, the following criteria have been regarded: the optimal superstructure of prefabricated concrete building systems; the particularity of developing countries (eg the climate, the political and economic conditions); the time for construction and necessary innovative developments for an economically optimised superstructure. In addition, alternative construction methods and costs for a simple but variable system which can be adapted to changing functions are suggested.

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)0.003
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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

Citations7
Published2007
Admission routes1
Has abstractyes

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