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Record W4408275413 · doi:10.38140/as.v12i1.1751

Efficiency in the provision of production specifications for the South African construction industry

2005· article· en· W4408275413 on OpenAlexaboutno aff
Tinus Maritz

Bibliographic record

VenueActa Structilia · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsProduction (economics)BusinessOperations managementEngineeringEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

In most developed countries production specifications are based on national standardised specification systems, such as the National Building Specification or NBS (Great Britain), the National Specification System or NATSPEC (Australia), Master Specification Systems or MasterSpec (United States of America and Canada), General Materials and Workmanship Specifications or AMA (Sweden), and the National Standard Building Specification or STABU (the Netherlands). Standard specifications are primarily designed to shorten descriptions in the texts of new projects, whether in respect of descriptions on architectural or engineering drawings and technical specifications or descriptions in bills of quantities, schedules of rates, etcetera. In some countries the development of computerised specification systems has reached the point that these systems are supplanting the traditional word processing method. These systems are also providing links or interfaces to other information systems of the construction sector, such as design, products and cost information systems, etcetera.The South African construction industry, however, lags behind these countries that have been involved in the development of construction information systems or processes over the years. A call is therefore made to improve the efficiency of providing production specifications, as inadequate project information has been identified as one of the major causes of inefficiency in the building process.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.900
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.135
GPT teacher head0.353
Teacher spread0.219 · 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 designOther design
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
Published2005
Admission routes1
Has abstractyes

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