DTCoach: Your Digital Twin Coach on the Edge During COVID-19 and Beyond
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
A Digital Twin (DT) is a digital replica of a living or non-living entity, called “real twin.” Data is collected from the real twin and analyzed using Artificial Intelligence (AI), which subsequently provides the real twin with valuable feedback. One of the most promising applications for humans is the DT for health and well-being [1]. Although the DT technology has been widely adopted in industries such as the manufacturing industry, where it has proven highly beneficial, its use in the domain of health is in its infancy. Few researchers have addressed the DT for health. Such is the work in [2] where a DT is proposed for heart disease detection, or in [3] that presents an ecosystem of the DT in the domain of well-being where the real twin's physical activity is measured by the digital twin, which then provides feedback in real-time to the real twin.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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