Overcoming the Data Availability Paradox with Managed Digital Twin Instances
Bibliographic record
Abstract
Digital transformation, IoT, and cloud storage have led to vast data collections that tend to exist in isolated silos, thus limiting their utilization. This leads to the data availability paradox, in which individuals, groups, and organizations possess vast amounts of data but struggle to utilize or share it effectively. The struggle is due to privacy, security, and intellectual property concerns. When this happens, individuals and institutions are unable to take full advantage of the capability of Industry 4.0 and digitalization. This paper proposes using managed Digital Twin Instances (DTI) to overcome this paradox. The fully managed digital twin instances provide well-defined and fully controlled access to models derived from the underlying data sets. Digital twin instances, therefore serve as virtual replicas of real-world systems, entities, or processes, enabling controlled access. A key issue in this approach is the effective hosting and managing large numbers of digital twin instances. The paper introduces a management and hosting framework and its evaluation in the MS Azure cloud environment.
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 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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".