Eco-labeled wood products in the U.S. residential construction industry: Architects’ awareness and usage of certified wood and green building programs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".