Is the Risk Comparison Made by the Public Between EMF and Smoking or Asbestos a Valid One?
Bibliographic record
Abstract
The possibility of adverse health effects from exposure to extremely low frequency (ELF) electric and magnetic fields (EMF) has caused considerable controversy in the scientific community and has received great attention in the media and among the general public with many comparing ELF EMF with tobacco smoking and asbestos. Although both smoking and asbestos are now classified by the International Agency for Research on Cancer (IARC) as Group 1 or “established” carcinogens, this was not always the case. In this paper the evidence for the carcinogenicity of ELF EMF is compared with that for smoking and asbestos using the Bradford Hill model for establishing causality between exposure and disease. Application of the model shows that present data are insufficient to demonstrate that exposure to ELF EMF poses a definite human health hazard. However, while the bulk of the evidence is weak, there are several epidemiological studies which have reported an association between prolonged exposure to magnetic fields at levels above what is normally encountered and an increased risk in childhood leukaemia. On this basis IARC has classified ELF magnetic fields as a Group 2B or “possible” carcinogen.
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.
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 | 0.083 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".