Enhancing characterization of water use practices in cement manufacturing and related construction sectors
Bibliographic record
Abstract
This work presents an investigation into water use patterns for cement manufacturing, ready-mixed concrete production, and buildings under construction and after occupation. Cement is the main component in making concrete, which is the most widely used structural building material in the world, and therefore plays an important role in global water use in the construction sector. The data collection methodology included review of refereed journals, analysis of published Corporate Social Responsibility Reports from worldwide cement companies, as well as case studies conducted in two cement plants (one in Brazil and one in Canada), thus incorporating real-world operating conditions. Analysis of water usage at ready-mixed concrete plants and buildings under construction and after occupation was also undertaken in Brazil. Water use at the two cement plants ranged from 250 to 2,000 litres per tonne of cement (compared to reported 147 to 3,500 L/tonne), indicating a wide range in water use patterns. Eleven stages of water use were identified for cement manufacturing, but accurate water use data could not be obtained for all these stages. Identifying and implementing water saving opportunities in cement manufacturing was hampered by a lack of reliable water use data. To address this, an approach was developed for categorizing levels of data reliability according to methods of data acquisition, and this approach was used to characterize the reliability of data compiled during this research. Reliability for the collected data in this study was then characterized to be between A+ to C-. The proposed data reliability approach can help improve data collection, reporting and decision-making around water conservation, both locally within manufacturing facilities and on jobsites, and at the level of governmental policy. This work therefore contributes to the field of water management by (a) shedding light on the lack of water usage data availability and reliability in the globally important sectors of cement manufacturing, concrete production, and buildings under construction and after occupation; (b) proposing an approach for improving the reliability of water usage data; (c) suggesting steps to improve knowledge of water usage in various sectors of construction industry; and (d) promoting best water management practices in this field.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".