ASSESSING THE ADOPTION OF CROSS LAMINATED TIMBER BY ARCHITECTS AND STRUCTURAL ENGINEERS WITHIN THE UNITED STATES
Bibliographic record
Abstract
ABSTRACT Cross Laminated Timber (CLT) is an engineered wood product for the construction industry offering multiple structural, environmental and supply chain benefits. CLT can be used for an entire building, as both the lateral and vertical load resisting system, or for select elements such as the roof, floors or walls. CLT products were developed in the early 1990’s and have been widely adopted throughout Europe, and more recently, in Canada. However, use of CLT products is still relatively rare in the US. We present the results of a nationwide phone survey in the US conducted with architects and structural engineers to gauge their awareness, rate of adoption and assimilation of CLT products. Although adoption of CLT amongst architects and structural engineers is still at a nascent level within the construction sector, awareness is high, with 100% of our sample respondents cognizant of CLT. Architects and structural engineers perceive relative advantages of using CLT as well as compatibility with traditional construction. However, the adoption process is impeded by issues associated with complexity, trialability and observability. Key barriers to adoption of CLT as perceived by these two stakeholders are lack of experience from construction stakeholders, lack of training and tools for construction management stakeholders, lack of client requests and CLT inventory.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".