Sustainable Ceramics: Creating Effective Key Performance Indicators for Industry Monitoring
Bibliographic record
Abstract
Sustainable development in the ceramic industry focuses on meeting present needs without compromising future resources. Key strategies include recycling waste, improving energy efficiency, and adopting green technologies. The materials used in creating ceramics, which are very abundant and renewable, are primarily sand or clay, unlike other materials such as real wood. This means that ceramic products are already environmentally friendly right from the start, before the manufacturing process commences. This study discusses environmentally sustainable recycled concrete, using ceramic waste as coarse aggregate in construction. It also discussed how the energy efficiency of buildings can be optimized through thermal energy storage and environmentally friendly materials. This also examined the analysis of Energy Management Systems (EMS) and how they can promote sustainability. The industry is changing with technological innovation using alkali- activated mortars, porous ceramics, and low-carbon technologies. In all their challenges of high energy consumption, green technologies and sustainable practices are crucial in lowering carbon footprints and promoting environmental responsibility. The focus has been made towards the various applications of the ceramic materials in different areas and the benefits associated with them. It can be concluded that the with better technological advancements and research on the design aspects, there is high scope of performance enhancement in the industrial areas.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".