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Record W2937109357 · doi:10.29173/mocs63

Comparing cross laminated timber with concrete and steel: a financial analysis of two buildings in Australia

2017· article· en· W2937109357 on OpenAlexvenueno aff
Dylan S. Cazemier

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsProfit marginCross laminated timberRevenueTimelineInternal rate of returnEquity (law)Rate of returnApartmentBuilding materialFinanceEngineeringBusinessCivil engineeringEconomicsMathematicsStatistics

Abstract

fetched live from OpenAlex

This research paper undertakes a comparative financial analysis of the performance of a Cross Laminated Timber (CLT) apartment building with a concrete and steel apartment building constructed in Australia. A product known as CLT has been found to be an effective alternative form of construction to building with the more traditional materials of concrete and steel. CLT can assist in reducing carbon emissions through carbon sequestration. Respective revenue, time and cost variables are calculated, analysed and compared per development. Financial modelling of these variables results in; Development Margin, Development Profit, Return on Equity (ROE) and Equity Internal Rate of Return (IRR). These figures are performance indicators used to appraise the development. The study supports the economic benefits when comparing CLT to concrete and steel construction. It is concluded that building with CLT may result in less development profit and margin, but an increased Equity IRR due to a reduced investment timeline.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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 designNot applicable
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

Citations17
Published2017
Admission routes1
Has abstractyes

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