Estimates of Natural Carbonation of Concrete across Canada
Bibliographic record
Abstract
As efforts towards net-zero emissions by 2050 of the cement and concrete sector, quantifying CO2 uptake in concrete due to natural carbonation and its impact on local carbon accounting warrant attention. This study investigated the CO2 uptake and CO2 uptake-to-emission ratio (%) due to natural carbonation in Canada by examining 12 different Canadian geographic locations (four urban, four rural, and four indigenous communities). Furthermore, 24 existing carbonation models were reviewed, and the Nilsson (2011) CO2 uptake model was selected. As model input data, concrete mix designs with varying fly ash and slag replacement level were applied, coupled with local data on relative humidity and CO2 concentration level. The carbonation depth, use-phase CO2 uptake, and CO2 uptake-to-emission ratio (%) over 50 years (2020 - 2070) were estimated for the 12 locations. Finally, the CO2 uptake-to-emission ratio (%) observed in selected locations are lower than the 23% global estimate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".