The Universal Classification system for assessing the embodied carbon of concrete in Canada
Bibliographic record
Abstract
Concrete is known to be the most prevalent human-made material globally owing to its durability, versatility, and affordability that, however, contributes significantly to global CO2 emissions. Consequently, efforts have intensified to reduce concrete’s embodied carbon, with emerging or conventional alternative technologies aiming to mitigate emissions and produce “sustainable” and “low carbon” concrete, a set of terms that have been used inconsistently leading to potential greenwashing and confusion in the industry. Simultaneously, there is a pressing need for tools and policies to guide the design of low-carbon concrete structures and infrastructure. This paper introduces an embodied carbon classification system as a practical solution to address industry confusion and facilitate sustainable practices in the concrete construction sector. It consists of a universally applicable tool that can be used by designers, manufacturers, asset owners, contractors, and policy makers to enable a robust evaluation of the embodied carbon concrete, develop pathways to concrete decarbonization, and set interregional definitions and targets for low-carbon concrete materials.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".