Assessment of occupational risks to extremely low frequency magnetic fields: Validation of an empirical non-expert approach
Bibliographic record
Abstract
The expert method of exposure assignment involves relying on chemists or hygienists to estimate occupational exposures using information collected on study subjects. Once the estimation method for a particular contaminant has been made available in the literature, it is not known whether a non-expert, briefly trained by an expert remaining available to answer ad hoc questions, can provide reliable exposure estimates. We explored this issue by comparing estimates of exposure to extremely low frequency magnetic fields (ELF-MF) obtained by an expert to those from a non-expert. Using a published exposure matrix, both the expert and non-expert independently calculated a weekly time-weighted average exposure for 208 maternal jobs by considering three main determinants: the work environment, magnetic field sources, and duration of use or exposure to given sources. Agreement between assessors was tested using the Bland-Altman 95% limits of agreement. The overall mean difference in estimates between the expert and non-expert was 0.004 μT (standard deviation 0.104). The 95% limits of agreement were - 0.20 μT and + 0.21 μT. The work environments and exposure sources were almost always similarly identified but there were differences in estimating exposure duration. This occurred mainly when information collected from study subjects was not sufficiently detailed. Our results suggest that following a short training period and the availability of a clearly described method for estimating exposures, a non-expert can cost-efficiently and reliably assign exposure, at least to ELF-MF.
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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 | 0.072 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".