Digital twin development towards integration into blue economy: A bibliometric analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.036 | 0.038 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it