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Record W2323510960 · doi:10.5558/tfc2014-124

Eco-labeled wood products in the U.S. residential construction industry: Architects’ awareness and usage of certified wood and green building programs

2014· article· en· W2323510960 on OpenAlexvenueno aff
Tait Bowers, Indroneil Ganguly, Ivan Eastin

Bibliographic record

VenueThe Forestry Chronicle · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsCertificationEnvironmentally friendlySustainabilityBusinessGreen buildingRenewable energyEnvironmental economicsArchitectural engineeringEngineering

Abstract

fetched live from OpenAlex

The use of environmentally friendly building materials has experienced slow growth within the residential construction market due to higher cost and low availability of certified wood. The development of green building programs (GBPs) marked the beginning of the effort to adopt energy-efficient design guidelines and utilize eco-friendly renewable materials in structures. These programs were targeted at reducing environmental impacts by integrating eco-friendly materials into the design and construction of buildings, including promoting the use of environmentally certified wood products (ECWPs) harvested from sustainably managed forests. This research was designed to determine which attributes influence architects’ decisions to use environmentally certified wood products in residential construction projects and how this might influence their perceptions and use of green building programs. The results indicate that architects who have participated in a GBP were more likely to have used ECWPs. The material attributes that influence architects’ selection of materials are mainly related to economics and function as opposed to environmental friendliness. These results will help to inform and broaden the understanding of issues that influence the adoption and utilization of environmentally certified wood products, and identify some of the factors that can contribute to their continued growth in the U.S. marketplace.

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 score0.060
Threshold uncertainty score0.472

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 studies0.0000.001
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 head0.016
GPT teacher head0.227
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 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

Citations12
Published2014
Admission routes1
Has abstractyes

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