Experiments to Determine the Most Effective Way to Map BIM Information Into Athena Software for Embodied Carbon Calculations
Bibliographic record
Abstract
We are in a climate emergency, and urgently need to reduce carbon emissions. Buildings are currently responsible for 39% of these emissions globally and it is critical that these be mitigated to restrict the impact of global warming. Embodied carbon refers to the greenhouse gas emissions arising from the manufacturing, transportation, installation, maintenance, and disposal of building materials and is a priority for reductions in the immediate term. This research calculates embodied carbon through Athena Impact Estimator for Buildings software using two methods. The first creates quantity takeoff for the building assemblies by using Revit software and imports them as modified Excel Bill of Materials (BOM) into the Athena software. The second creates building assemblies in Athena software using BIM assembly types and quantity takeoffs. The Smart Campus Integration and Testing Hub (SCITHub) project was used as a case study. It was observed that Revit software does not have the ability to create building assemblies in extensive details as the Athena software has. Revit software can be further developed to produce detailed assemblies as per the Athena software properties. Besides, the Athena software can also be developed to contribute to add further details into the Revit imported BOMs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".