<i>Retracted March 9, 2026:</i> AECT: Accurate Energy Efficient Contact Tracing Using Smart Phones for Infectious Disease Detection
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Contact tracing is an important technique to reduce the impact of infectious diseases in smart cities. Smart phones equipped proximity sensors can be used to enable contact tracing, however accuracy of detection and energy efficiency is a key challenge. To address this challenge, we propose an accurate energy-efficient contact tracing (AECT) algorithm that detects which users came in contact with an infected user by performing computations at the server-side. Additionally, the AECT algorithm uses the wireless scan method, which calculates proximity based on pseudo-range multilateration and makes relevant comparisons with the matching score (MS) method based on the computation of received signal strength indication (RSSI) metric. Simulation results demonstrate that the scan method (AECT) is highly accurate and outperforms the scan method, highlighting that real distance is a better metric in contact tracing than a proxy for distance such as RSSI. Lastly, simulation results also demonstrate that the scan method (AECT) is 16 times more energy-efficient than the baseline 1 Hz frequency method, and we recommend it as a method of choice for performing contact tracing against infectious diseases such as COVID-19.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.224 | 0.216 |
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".