Advancing Basalt Fiber-fly Ash/Marble Dust Composites: A Systematic Review and Research Horizons
Bibliographic record
Abstract
Introduction: Materials are crucial in the everyday existence of individuals. Nevertheless, the changing requirements of societies emphasize the necessity for the ongoing advancement of innovative materials. There has been a recent focus on composites made from natural fibers. Fly ash and marble dust can be economically beneficial alternatives to other mineral fillers. Method: This analysis included all relevant Scopus-indexed articles on the topic and was refined to a final set of 90 documents for detailed analysis. Systematic, bibliometric, co-occurrence, and thematic analyses were employed. Results: Construction and Building Materials was found to be the top journal in the discipline. China, India, Australia, and Canada were leading in terms of the number of publications. The keywords 'fly ash', 'compressive strength', and 'slags' were identified as the most prominent terms in the analyzed research studies. Discussion: This study aimed to conduct a structured, bibliometric, and thematic review of the literature on basalt fiber-fly ash/marble dust composite. The SciMAT tool facilitated the analysis of themes and concepts in the field, showcasing their evolution over time. The future research path has also been identified. Conclusion: This article has provided a detailed account of the research advancements in fly ash/marble dust-filled basalt fiber-reinforced epoxy polymer composite materials. It has clearly identified important issues and predicted future trends in the 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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.032 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".