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Record W4296078138 · doi:10.29173/mocs279

Decision factors for the feasibility study of developing a prefabrication plant

2022· article· en· W4296078138 on OpenAlexvenueno aff
Monika Patel, SeyedReza RazaviAlavi

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsnot available
Fundersnot available
KeywordsPrefabricationFlexibility (engineering)IncentiveGovernment (linguistics)Process (computing)BusinessOperations managementEngineeringRisk analysis (engineering)Process managementEnvironmental economicsComputer scienceCivil engineeringEconomicsManagement

Abstract

fetched live from OpenAlex

A feasibility study is one of the key phases for developing a prefabrication plant, which requires an analysis of many influencing factors. This research aims to identify the main decision factors for the feasibility study of developing a prefabrication plant. To this end, a thorough literature review and semi-structured interviews with the subject matter experts (SMEs) from different countries were conducted. Analyzing the findings, this study identified 35 decision factors for the feasibility study, such as upfront costs, operation and maintenance costs, market demands, government incentives, design flexibility, plant location, logistics management and costs, and technological constraints. The results of this study will help the industry practitioners, who are seeking to develop prefabrication plants, in their decision-making process during the feasibility study phase.

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)0.054
metaresearch head score (Gemma)0.075
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.075
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0040.004
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 head0.045
GPT teacher head0.257
Teacher spread0.211 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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