Bibliographic record
Abstract
Escalating level of greenhouse gases (GHGs) has contributed to global climate change, among which CO2 is mainly responsible for global warming. Cement industries are responsible for 7-8% of global CO2 emissions during cement manufacturing and operations, which involves calcination of raw material (i.e. limestone) at high temperatures to produce clinker and combustion of fuel. Therefore, for the cement industries globally, CO2emission mitigation strategies need to be taken into account in their strategic outlook that can help reduce the environmental footprint. In the past, using supplemental cementitious materials (SCMs) to lessen carbon footprints in the construction industry has already been thoroughly investigated. Due to the limited availability of SCMs resources in long term, other innovative ways need to be identified. Recently, carbon capture and utilization is pitched as the encouraging driver that can address the real problem of challenging emissions. CO2 sequestration in concrete is a promising strategy for lowering carbon footprint and providing a loop for the carbon flow to achieve a more sustainable construction practice. CO2 sequestration is performed in two ways; First, by injecting CO2 into ready-mix concrete, and second by enforced curing of prefabricated building components under CO2 conditions. Both ways have the potential to reduce net emissions by capturing CO2 and storing it in mineral form (carbonates) in the cementitious matrix. This process can improve the properties of the concrete in addition to capturing CO2 inside of it.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".