Low-carbon concrete: sustainable performance at an affordable price
Bibliographic record
Abstract
The Canadian Government has set ambitious targets to reduce GHG emissions by 2025 and achieve net-zero emissions by 2050 to address the climate crisis. The construction industry must undergo a significant decarbonization process to help mitigate the climate crisis. This white paper provides information on general approaches that have been well-known or widely used in lowering the embodied carbon of concrete materials, as well as cost, without compromising performance or safety. It addressed some common perceived risks of using low-carbon concrete and discussed how current standards support low-carbon concrete materials in construction projects. Understanding that great efforts are being undertaken globally in developing low-carbon concrete, which is a fast-evolving area and critical to reducing GHG emissions in the construction sector, it is not the intention of the paper to discuss the new, emerging and promising innovations. This white paper seeks to facilitate the procurement and implementation of concrete in construction projects across Canada that has lower embodied carbon, based on the evidence of the existing technologies and approaches that are widely accepted but may not have been seen as the low-carbon strategies among the concrete and structure engineers’ communities. By increasing the use of low-carbon concrete, we can help reduce the construction industry's carbon footprint and move towards a more sustainable future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".