Bibliographic record
Abstract
The COVID-19 pandemic has significantly threatened the health and well-being of humanity. Contact tracing (CT) as an important non-pharmaceutical intervention is essential to containing the spread of such an infectious disease. However, current CT solutions are fragmented with limited use of sensing and computing technologies in a scalable framework. These issues can be well addressed with the use of the Internet of Things (IoT) technologies. Therefore, we need to overview the principle, motivation, and architecture for a generic IoT-based CT system (IoT-CTS). A novel architecture for IoT-CTS solutions is proposed with the consideration of peer-to-peer and object-to-peer contact events, as well as the discussion on key topics, such as an overview of applicable sensors for CT needs arising from the COVID-19 transmission methods. The proposed IoT-CTS architecture aims to holistically utilize essential sensing mechanisms with the analysis of widely adopted privacy-preserving techniques. With the use of generic peer-to-peer and object-to-peer sensors based on proximity and environment sensing mechanisms, the infectious cases with self-directed strategies can be effectively reduced. Some open research directions are presented in the end.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".