Digital twin development towards integration into blue economy: A bibliometric analysis
Bibliographic record
Abstract
Digital Twin (DT) technology plays a crucial role in the modernization and optimization of numerous industrial sectors. The blue economy encompasses established sectors such as marine energy systems, shipbuilding and operation, aquaculture and fisheries, and emerging areas including coastal protection and deep-sea mining. Many of these sectors are crucial for attaining Sustainable Development Goals (SDGs), especially pertaining to climate action and marine biodiversity. The integration of DT technologies within the blue economy can offer added value by enhancing operational efficiency, improving risk management, and fostering sustainable practices. This paper uses bibliometric research methods to provide a state-of-the-art overview of this research area. Insights are obtained through several bibliometric indicators, including publication trends, country-based distribution patterns of scholarly communications, and research impact through citation analysis. Keyword co-occurrence analysis is carried out to identify key research themes within the main blue economy sectors. This analysis will enable the research community to understand the key research themes, trends, major research hotspots, and influential works to provides a foundation for innovation, efficiency, and sustainability, benefiting researchers and industry actors. Additionally, it provides policy makers with evidence-based insights crucial for crafting informed policies that promote sustainable development within the blue economy. • A bibliometric analysis of Digital Twin integration within blue economy sectors is presented. • Advancements in Digital Twin technology across various blue economy sectors are emphasized. • Main research areas, emerging trends, key knowledge sources and stakeholders are identified. • Multiple directions for future research in this domain are discussed.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.179 | 0.292 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".