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Record W2936010761 · doi:10.29173/mocs67

Assessment on the carbon efficiency in the construction stage: a comparative study between prefabricated and conventional construction

2017· article· en· W2936010761 on OpenAlexvenueno aff
Wenjun Gao, Chao Mao

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsPrecast concreteCarbon fibersEnvironmental economicsConstruction industryKey (lock)Construction engineeringPrefabricationCivil engineeringArchitectural engineeringEngineeringComputer scienceEconomics

Abstract

fetched live from OpenAlex

Construction industry is the consuming large amounts of natural resources and at the expense of a heavy environmental burden. Therefore, we need to keep a balance between creating economic benefit through construction and focusing on the influence to the environment with the aim of the value of carbon emissions maximize. The paper puts forward carbon efficiency which provides a linkage between carbon reduction and value creation of construction effectively can reflect construction efficiency. The essence of carbon efficiency is using the lowest environment output to build a construction. Through analysing two cases, result shows that carbon efficiency of prefabricated construction is higher than the conventional construction’s, which improves 25%. Besides, the measures are provided to improve the carbon efficiency of constructing. Enhancing the precast level, implementing prefabricated components standardized, optimizing site management is the main key to realize the low carbon construction.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.941

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.274
Teacher spread0.249 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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