Recent Advances in Research on Radiofrequency Fields and Health: 2004–2007
Bibliographic record
Abstract
The widespread use of wireless telecommunications devices, particularly mobile phones and wireless networks, has resulted in increased human exposure to radiofrequency (RF) fields. Although national and international agencies have established safety guidelines for exposure to RF fields, concerns remain about the potential for adverse health outcomes to occur in relation to RF field exposure. The extensive literature on RF fields and health was reviewed by a number of authorities, including the Royal Society of Canada (1999). This report is the third in a series of updates to the original report of the Royal Society of Canada, covering the period 2004-2007. In particular, the present study examined new data on (1) dosimetry and exposure assessment, (2) biological effects of RF fields such as enzyme induction, and (3) toxicological effects, including genotoxicity and carcinogenicity. Epidemiological studies of the potential health effects of RF exposure, particularly from mobile phones, were determined, along with human and animal studies of neurological and behavioural effects. Within the last 4 yrs investigators concluded that there is no clear evidence of adverse health effects associated with RF fields, although continued research is recommended to address specific areas of concern, including exposure to RF fields among children using mobile phones. The results of the ongoing 13-country World Health Organization INTERPHONE study of mobile phones may provide important new information on the potential cancer risks associated with mobile phone use.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".