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Record W2594236441 · doi:10.3390/app7020180

Construction Industrialization in China: Current Profile and the Prediction

2017· article· en· W2594236441 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Sciences · 2017
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of ChinaMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsIndustrialisationChinaDelphi methodRegional scienceConstruction industryEconomic growthBusinessGeographyEngineeringPolitical scienceEconomicsConstruction engineeringComputer science

Abstract

fetched live from OpenAlex

The ongoing undertaking of construction industrialization in China is redefining the industry and creating a new era for building construction. In order to identify the construction industrialization status and progress, a national survey is conducted across 19 key provinces and municipalities in China. Based on the collected data, construction industrialization is analyzed from various perspectives: (1) the industrialized building floor area is profiled using maps with colours showing the different levels of construction industrialization in China as of 2014; and (2) structural types and building types are analyzed for industrialized construction, and it is found that reinforced concrete is the predominant structure type, accounting for 77.1% of total floor area of industrialized construction in 2014. The industrialization trends are also predicted for the following five years using Holt’s and Delphi method. This research reveals the status and the promising trends of construction industrialization in China.

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.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.268

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.258
Teacher spread0.239 · 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