Lowering the Carbon Footprint of Concrete by Reducing Clinker Content of Cement
Bibliographic record
Abstract
Significant efforts have been made to reduce carbon dioxide (CO 2 ) emissions associated with the manufacture of portland cement, primarily by making the process more energy efficient and increasing the use of alternative fuels. Further reductions in CO 2 can be achieved by lowering the clinker component of the cement because the pyroprocessing used to manufacture clinker produces approximately 1 tonne of CO 2 for every tonne of clinker. Traditionally reductions in the clinker content of cement have been achieved by producing blended cement consisting of portland cement combined with a supplementary cementing material (SCM). In Canada, it is now permitted to intergrind up to 15% limestone with cement clinker to produce portland limestone cement or blended portland limestone cement. Recent trials were conducted at the Brookfield cement plant in Nova Scotia to evaluate the performance of a blended cement containing 15% ground, granulated blast furnace slag (an SCM) with that of a blended portland limestone cement containing the same amount of slag plus 12% interground limestone. Performance was evaluated by the construction of a section of concrete pavement using concrete mixtures produced with the two cements and various amounts of fly ash (another SCM). A wide range of laboratory tests were performed on the concrete specimens cast on site during the placement of the concrete pavement. The results indicated that the cements were of equivalent performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".