Analysis of mobile phone design features affecting radiofrequency power absorbed in a human head phantom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The US FCC mandates the testing of all mobile phones to demonstrate compliance with the rule requiring that the peak spatial SAR does not exceed the limit of 1.6 W/kg averaged over any 1 g of tissue. These test data, measured in phantoms with mobile phones operating at maximum antenna input power, permitted us to evaluate the variation in SARs across mobile phone design factors such as shape and antenna design, communication technology, and test date (over a 7-year period). Descriptive statistical summaries calculated for 850 MHz and 1900 MHz phones and ANOVA were used to evaluate the influence of the foregoing factors on SARs. Service technology accounted for the greatest variability in compliance test SARs that ranged from AMPS (highest) to CDMA, iDEN, TDMA, and GSM (lowest). However, the dominant factor for SARs during use is the time-averaged antenna input power, which may be much less than the maximum power used in testing. This factor is largely defined by the communication system; e.g., the GSM phone average output can be higher than CDMA by a factor of 100. Phone shape, antenna type, and orientation of a phone were found to be significant but only on the order of up to a factor of 2 (3 dB). The SAR in the tilt position was significantly smaller than for touch. The side of the head did not affect SAR levels significantly. Among the remaining factors, external antennae produced greater SARs than internal ones, and brick and clamshell phones produced greater SARs than slide phones. Assuming phone design and usage patterns do not change significantly over time, we have developed a normalization procedure and formula that permits reliable prediction of the relative SAR between various communication systems. This approach can be applied to improve exposure assessment in epidemiological research.
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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 | 0.001 |
| 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