Guest Editorial Special Issue on Industrial Metaverse for Smart Manufacturing
Bibliographic record
Abstract
The industry is undergoing a transformation toward smart manufacturing, fostering intelligent operations, sustainability, and digitalization. However, the current state of the process industry falls short of this future vision. Key areas, such as hybrid modeling, autonomous control, dynamic scheduling, intelligent decision making, security and safety control, and predictive maintenance, still require significant development. Given that the industrial metaverse enables the virtualization and digitization of industrial processes using technologies, such as artificial intelligence, blockchain, cloud computing, and digital twins, it is promising to establish the industrial metaverse for manufacturing, encompassing the entire lifecycle based on the industrial Internet and other modern information technologies.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.029 | 0.022 |
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".