Bill of materials (BoM) and archetype information for buildings in Canada
Bibliographic record
Abstract
Significant progress has been made globally in reducing GHG emissions from the operation of buildings, however, huge challenges still remain in mitigating the embodied emissions from the manufacturing, transportation, and disposing of building materials. This is particularly relevant in BC, where electricity is largely generated from renewable sources, indicating limited potential for further reducing GHG emissions from building operations. Therefore, investigating the options to reduce embodied GHG emissions in building materials presents another crucial opportunity to further mitigate the overall GHG emissions from buildings. For example, the City of Vancouver’s newly released Climate Emergency Action Plan has set an ambitious goal of reducing 40% embodied GHG emissions in new buildings compared to the 2018 benchmark. To support decision-making that could ultimately fulfill such an ambitious goal, it is imperative that a standard approach is used to derive benchmark buildings and the corresponding bill-of-materials (BoM). Accordingly, we compiled a BoM dataset of 35 typical buildings in Canada. The data was classified into “whole-building level” and “assembly-level”, and building materials were sorted by an aggregation system (see below) in both classifications. Whole-building-level BoM contains data for 33 buildings, including institutional buildings and residential houses at the University of British Columbia, container-based single-family housing, single-family residential building, precast concrete commercial buildings, etc. On the other hand, assembly-level BoM contains material data for different structural components of one multi-unit apartment and one typical newly-built single-family home in Vancouver. The aggregation system organizes the material data by three tiers - M1, M2, and M3, which offers 3 hierarchical levels of specificity. The first hierarchical level (M1) provides the least specified information while the final level (M3) provides the most detailed information. For example, Aluminum cold-rolled sheet (M3) is categorized within Aluminum (M2) under Metal (M1). This aggregation system offers the flexibility for LCA practitioners to obtain BoM information at the resolution that fits their scope of work.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.024 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".