Translating building design information into embodied carbon information: gaps, constraints, and recommendations
Bibliographic record
Abstract
Over the years, different software has been developed to design and analyze building information. With an increased understanding of the importance of embodied carbon as a driver of climate change, there is increasing demand to easily quantify building embodied carbon. This major research project examines how materials are defined in Revit, a building information model and how it aligns with carbon accounting material definitions required as illustrated by Athena. This research is done in order to support more accurate and detailed embodied carbon analysis during the design phase. Constraints and information gaps are identified and used to develop a better understanding of whether a case study model with a level of development of 300 has materials sufficiently defined to map to Athena. This method is tested on the Toronto Metropolitan University Smart Campus Integration and Testing Hub (SCITHub), using inputs provided by the design team and vendors, to evaluate its effectiveness. Although the necessary Athena properties may be established using a BIM, it is discovered that Revit does not provide key Athena required input in an easily readable format that can be effectively incorporated into carbon accounting software.
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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 | 0.027 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".