Materials Used in the Construction of Artificial Reefs: A Bibliometric Review
Bibliographic record
Abstract
Artificial reefs (ARs) have been widely employed around the world as a strategy for marine conservation, biodiversity enhancement, and climate change mitigation. However, the selection and evaluation of materials used in their construction still lack standardization. This study presents a systematic and bibliometric review of the scientific literature on materials used in AR construction, focusing on sustainable, bioreceptive, and technologically innovative solutions. A total of 309 articles published from 2004 to 2024 were analyzed, retrieved from the Scopus and Web of Science databases using the Biblioshiny platform. The research included analyses of annual scientific production, keyword co-occurrence, thematic trends, and identification of the most relevant authors and institutions. As a novel contribution, a taxonomy of used materials was proposed, developed by a pattern-matching methodology based on Nickerson et al. (2013), and organized into five categories: natural, recycled, polymeric, cementitious, and innovative composites. The qualitative analysis highlighted the importance of concrete, which remains widely used but increasingly combined with industrial wastes and techniques such as 3D printing, self-healing concretes, and carbonation curing. Despite technical advances, only one study applied Life Cycle Assessment (LCA), revealing a significant gap in measuring the environmental impacts of these solutions. The originality of this review lies in its integration of bibliometric analysis, technical evaluation, and conceptual systematization of materials, providing a structured foundation for future research and practical applications. The development of more effective and sustainable artificial reefs depends on the adoption of integrated ecological and constructive criteria, as well as the broader application of robust environmental metrics.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.185 | 0.199 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".