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Record W1967083376 · doi:10.1061/41109(373)155

Canadian Industry Practitioners Perception on LEED Credits

2010· article· en· W1967083376 on OpenAlexaffabout
Ferah Rahman, Farnaz Sadeghpour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsToronto Metropolitan UniversityUniversity of Calgary
Fundersnot available
KeywordsSustainabilityAcknowledgementIncentiveScheduleGovernment (linguistics)BusinessPerceptionPosition (finance)Environmental economicsMarketingEconomicsComputer scienceManagementFinance

Abstract

fetched live from OpenAlex

While the need for sustainable practices in building projects is globally recognized, some construction stakeholders still demonstrate resistance in adopting sustainable practices. The general perception is that attempting to achieve sustainability will increase the capital cost of construction and can delay the schedule. In recent years, societal pressure and government incentives as well as practitioners knowledge and acknowledgement, have led many construction projects to pursue sustainability credits. In 2002, Canada Green Building Council (CaGBC) adopted the Leadership in Energy and Environmental Design (LEED) standard as a tool to evaluate the sustainability of construction projects. The main objective of this research is to generate a better understanding of LEED professionals' perception of the impact of achieving LEED credits on projects parameters such as cost, schedule and future value. A survey was conducted to clarify their position towards the impact of each of the 69 points in the early version of LEED-New Construction on project factors. The survey targets factors in its three phases — prior to construction, during construction and post construction. The results can assist in improving the evaluating systems and developing sustainability design tools in the future.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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.

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

Citations9
Published2010
Admission routes2
Has abstractyes

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