COVID-19 Contact Tracing Using BLE and RFID for Data Protection and Integrity
Bibliographic record
Abstract
Responding to the rapid spread of the COVID-19 virus, the need for contact tracing and self-isolation has become the focus of many health experts and governments as being the primary option for containing the spread of the disease. The utilization of the smartphone has been the focus of many efforts by creating mobile applications that harness the potential of GPS and BLE technologies to make contact tracing as efficient and effective as possible. The prevailing issue with this system is the concern of privacy and data protection of app users. In order to address this problem, through this paper, we are suggesting the use of passive RFID technology similar to that of most public transit systems. This system contains a client-held RFID card and a receiver, and a server that processes the data. Through this paper, we hope to present an alternative, less invasive system that will help governments and health officials prevent the spread of COVID-19 in their communities.
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".