Oregon design professionals views on structural building products in green buildings: implications for wood
Bibliographic record
Abstract
Buildings have been shown to have impacts on the environment. Consequently, green building rating systems have become a tool to help reduce these impacts. The objectives of this study were to identify gaps in information and access to green building materials as viewed by Oregon design professionals. The scope was limited to the major structural materials: concrete, steel, and wood. This article focuses on the results unique to wood products. Information was collected through group interviews. Each group was composed of professionals representing different aspects of material selection and construction of different scales. The results showed that structural material selection is driven by building code, cost, and building performance requirements. The environmental performance of the material was not considered. However, once the material was selected, designers tried to maximize environmental performance. The results showed that green building rating systems do not influence structural material selection, and interviewees noted that there is room for improvement in this area. Respondents had a positive view of wood and a strong desire to use more wood, particularly Forest Stewardship Council certified wood. Wood was viewed as the most sustainable structural material available. However, there were some concerns about wood products, with formaldehyde emissions being the most significant.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".