O38-3 Development of a source-exposure matrix for occupational exposure assessment of electromagnetic fields in the interocc study
Bibliographic record
Abstract
<sec><st>Introduction</st> To estimate occupational exposures to electromagnetic fields (EMF) for the INTEROCC study, we constructed a database of source-based measurements from published and unpublished literature. The aim of the current work was to summarise these measurements into a source-exposure matrix (SEM), accounting for their quality and relevance. </sec> <sec><st>Methods</st> We developed methods for combining available measurements, weighting the pooled estimates by our confidence in these data. Arithmetic and geometric means, as well as estimates of variability and maximum exposure were calculated by EMF source, frequency band and dosimetry type. </sec> <sec><st>Results</st> The SEM contains confidence-weighted exposure estimates for the electric and magnetic fields for 312 EMF exposure sources (from 0 Hz to 300 GHz). Operator position geometric mean electric field levels for RF sources ranged between 0.8 V/m (plasma etcher) and 320 V/m (RF sealer), while magnetic fields ranged from 0.02 A/m (speed radar) to 0.6 A/m (microwave heating). For ELF sources, electric fields ranged between 0.2 V/m (electric forklift) and 11,700 V/m (HVTL-hotsticks), while magnetic fields ranged between 0.14 µT (visual display terminals) and 17 µT (TIG welding). </sec> <sec><st>Conclusion</st> The methodology developed allowed the construction of an EMF-SEM and may be used to summarise similar exposure data for other physical or chemical agents. The SEM will be used together with detailed information on distance to the source, automation, and other determinants of exposure reported by the study subjects, to calculate indices of cumulative exposure to EMF for their use in the analysis of brain tumours risk associated with these exposures. The SEM will also be offered publicly for its use by other researchers. </sec>
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".