Health Risks of Electromagnetic Fields. Part III: Risk Analysis
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 management of potential health risks from electromagnetic (EM) fields presents both scientific and nonscientific challenges. When the scientific evidence is ambiguous, as is the case with EM fields, expert judgment of this evidence becomes particularly important. This article provides biomedical researchers with a comprehensive assessment of the status of EM health risk based on our two previous articles [Parts I and II, Critical Reviews in Biomedical Engineering, Volume 31, Issue 3]. Ambiguous evidence also necessitates rigorous public debate. This article also discusses effective risk communication approaches that play a key role in the EM risk issue. Because of uncertainty about health risks associated with EMF exposure, the public is more likely to experience difficulty in evaluating the available information and rely more on perceptions than facts when drawing conclusions. Even the most effective risk communication approaches are not likely to clarify all of the subtleties surrounding EM fields as a population health issue. Thus it is essential that all stakeholders involved in this issue participate in developing consensus solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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