Recent advances in the concrete industry to reduce its carbon dioxide emissions
Bibliographic record
Abstract
Increasing sustainability awareness has put the concrete industry in the spotlight to reduce its carbon dioxide emissions. Most of the carbon dioxide emission from the concrete industry is from the production of Portland cement which is the main binder in concrete, and the transportation of materials. Also, the production of other components in concrete such as aggregates, admixtures, and construction processes contribute to the industry's emission. In addition, the concrete industry is one of the major consumers of natural resources, and the increasing production of concrete has posed a huge strain on the natural reserve of these resources. Nevertheless, the last decade has seen several promising initiatives taken by the industry to improve its sustainability in order to achieve a net-zero emission by 2050. These initiatives vary from using alternative materials such as waste materials, optimizing concrete production processes, use of alternative sources of energy, etc. In order to create more awareness within the construction industry and its stakeholders, this paper explored various ways in which the industry is tackling these sustainability issues. The prospects alongside the challenges for these initiatives are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".