Health Risks of Electromagnetic Fields. Part I: Evaluation and Assessment of Electric and Magnetic Fields
Bibliographic record
Abstract
Exposure to electric and magnetic fields (EMF) emanating from the generation, distribution, and utilization of electricity is widespread. The major debate in recent years has focused on the possibility that exposure to EMF may result in adverse health consequences, including the development of cancer. This article provides a review and evaluation of potential health risks associated with residential and occupational exposure to EMF. In addition to reviewing data from laboratory, epidemiology, and clinical studies, we examine exposure data from field measurement surveys and exposure guidelines that have been established for EMF. Currently, the evidence in support of an association between EMF and childhood cancer is limited, although this issue warrants further investigation. Evidence of an association between EMF exposure and adult cancers, derived largely from occupational settings, is inconsistent, precluding clear conclusions. There is little evidence of an association between EMF and noncancer health effects. Epidemiological studies of EMF and population health are limited by exposure measurement error and the lack of a clear dose/response relationship in studies suggesting possible health risks. Further research is needed to clarify the ambiguous findings from present studies and to determine if EMF exposure poses a health risk.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".