Radiofrequency Energy Exposure from the Trilliant Smart Meter
Why this work is in the frame
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Bibliographic record
Abstract
This paper reviews radiofrequency (RF) field levels produced by electric utility meters equipped with RF transceivers (so-called Smart Meters), focusing on meters from one manufacturer (Trilliant, Redwood City, CA, USA, and Granby, QC, Canada). The RF transmission levels are summarized based on publicly available data submitted to the U.S. Federal Communications Commission supplemented by limited independent measurements. As with other Smart Meters, this meter incorporates a low powered radiofrequency transceiver used for a neighborhood mesh network, in the present case using ZigBee-compliant physical and medium access layers, operating in the 2.45 GHz unlicensed band but with a proprietary network architecture. Simple calculations based on a free space propagation model indicate that peak RF field intensities are in the range of 10 mW m or less at a distance of more than 1-2 m from the meters. However, the duty cycle of transmission from the meters is very low (< 1%). Limited measurements identified pulses from the meter that were consistent with data reported by the vendor to the U.S. Federal Communications Commission. Limited measurements conducted in two houses with the meters were unable to clearly distinguish emissions from the meters from the considerable electromagnetic clutter in the same frequency range from other sources, including Wi-Fi routers and, when it was activated, a microwave oven. These preliminary measurements disclosed the difficulties that would be encountered in characterizing the RF exposures from these meters in homes in the face of background signals from other household devices in the same frequency range. An appendix provides an introduction to Smart Meter technology. The RF transmitters in wireless-equipped Smart Meters operate at similar power levels and in similar frequency ranges as many other digital communications devices in common use, and their exposure levels are very far below U.S. and international exposure limits.
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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 | 0.000 |
| 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