Producing Alternative SCMs through Carbon Utilization .pdf
Bibliographic record
Abstract
Producing a High-Performance Low Carbon ASCM using Carbon Utilization Technology Authors Mr. Dhwanil Trivedi - United States - Carbon Upcycling Technologies Inc. Mr. Dante Luu - Canada - Carbon Upcycling Technologies Inc. Abstract In the current climate crisis, there is significant pressure on the cement and concrete industry to achieve net zero by 2050. Carbon Upcycling Technologies (CUT) has developed a patented technology to upcycle industrial byproducts into quality Alternative Supplementary Cementitious Materials (ASCM). One prominent Supplementary Cementitious Material (SCM) is Fly Ash (FA). However, the production of fresh fly ash is decreasing as companies move towards more environmentally friendly alternatives, there is a need to investigate how to reclaim FA from pits and landfills. CUT has developed a process to upcycle reclaimed FA into a high-performance ASCM, that meets or exceeds current industry specifications. This white paper will focus on a case study with the Minnesota Department of Transportation and the National Road Research Alliance deploying sustainable and durable concrete that meets or exceeds the current DOT specifications while having a lower global warming potential. CUT submitted a concrete mix that utilized its enhanced FA ASCM. For this project, CUT optimized the control mix through a 12.5% reduction in total cementitious material, with a 30% addition of enhanced FA. Discussed further in this paper is the 1-year strength and durability data that meet or exceed the control mix data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.023 |
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".