Valorization Of Industrial Wastes For Building Applications With The Added Benefit Of Carbon Sequestration
Bibliographic record
Abstract
With the world's current high output of industrial goods, hundreds of millions of tons of manufacturing by-products end up amassing in landfills each year. Steel-slag is not recycled to any significant degree, and even banned as a construction material in certain countries, such as Canada. Like many industrial waste residues, this limitation is mainly attributed to a lack in performance criteria permitting its economic and safe reuse. Moreover, the steel industry is a major contributor to anthropogenic CO2, and is subject to increasingly harsher regulatory codes that mandate heavier emission reductions. This project introduces a value-adding carbonation treatment that substantially enhances the waste slag's physical properties and, hence, its recyclable potential, while also presenting the added benefit of sequestering CO2. The end-use of the valorized slag as an aggregate replacement in concrete is explored. Considering that concrete is the world's most used construction material (> 9 billion tons per year), this project presents a sustainable building practice that fits within holistic environmental initiatives related to waste recycling, carbon mitigation, and resource conservation. In terms of practicality, an 8" concrete masonry block prepared in the prescribed manner will potentially sequester up to 2kg of CO2. The project ultimately seeks to demonstrate the possibility of implementing a closed loop system, for relevant industries, whereby waste streams and CO2 can be locally consumed at point source.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".