Instantaneous Viral Detection of SARS‐CoV‐2 and Beyond using Electromagnetic Sensing
Bibliographic record
Abstract
Abstract This study proposes a highly sensitive portable device that utilizes electromagnetic waves and data analytics for instantaneous Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS‐CoV‐2) detection. The device consists of a Radio Frequency (RF) circuit that interprets reflected and transmitted electromagnetic waves to identify virus signatures in physiologically significant matrices, including human saliva and diluted nasopharyngeal swabs. The sensor's accuracy is validated in both pre‐clinical and clinical settings, where clinical measurements demonstrate an instantaneous detection accuracy of 94%, sensitivity of 95%, and specificity of 97.5% between the sensor's physical parameters and SARS‐CoV‐2 detection. The sensor's accurate real‐time response is due to its unique design and precise modeling techniques. In addition, the same sensing system is tested across different viruses and its ability to differentiate between influenza A, respiratory syncytial, and SARS‐CoV‐2 viruses is proven. Hence this work presents a holistic system that can predict the viral concentration of SARS‐CoV‐2, as well as differentiate between different viruses instantaneously and without adding any amplifying agent.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".