Investigating embodied carbon of common floor ceiling assemblies and low carbon buildups to meet new requirements
Bibliographic record
Abstract
The buildings and construction sector is known to be the largest global emitter of greenhouse gasses, representing 37% of all emissions. International building regulations are poised to incorporate embodied carbon targets for new building construction, with restrictions expected to become stricter over time. California became the first state to add embodied carbon emission control as part of the California Green Building Standards Code. Some cities are also enacting policies like Toronto where new city owned buildings must demonstrate less than 350kg/CO2/m2. With this increased focus and requirements, whole building life cycle assessments will become an important tool in the architecture, engineering and construction (AEC) design process. These evolving environmental regulations demand a deeper understanding of the relationship between the acoustic performance and embodied carbon of common assemblies. This work compares this relationship in several construction types, including concrete, mass timber, and wood frame assemblies. This relationship is evaluated by using environmental product declarations (EPDs) and acoustic testing of various assembly types to offer a holistic comparison of the performance metrics. The findings will support development of best practices for selecting materials and acoustic design approaches to balance low embodied carbon with high acoustic performance and support the construction industry to meet future environmental standards.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".