The determinants of Canadian children's personal exposures to magnetic fields
Bibliographic record
Abstract
Study of the health effects of magnetic fields often depends on identifying determinants and hence indicators of personal exposure. This study identified determinants of children's exposure to magnetic fields and constructed a prediction model for them. For 632 children participating in a case-control study of childhood leukemia, we made direct measures of exposure over 48 h using a portable device, together with observations on candidate determinants. A child's age and sex, the proportion of time spent in the home, and their parents' education or income were very weak predictors of (logged) mean 48 h magnetic field (R(2) < 1%). More important were province (R(2) = 8.0%) and type of residence (R(2) = 11.3%). Low temperatures at the time of measurement were associated with high fields (about 20% increase for each 10 degrees C below 14, R(2) = 4.9%). Several visible attributes of wiring around residences predicted exposure, mostly captured in the Wertheimer-Leeper wire code (R(2) = 13.5%). Stationary 24 h measurement in the bedroom (R(2) = 63.3%) and spot measurements outside the house (R(2) = 40.7%) predicted personal exposures best. Adding other minor predictors increased only slightly variance explained by 24 h stationary (R(2) = 66.2%) and spot (R(2) = 46.8%) measurements. Without spot or stationary measurements, the best model was much less powerful (R(2) = 29.0%). We conclude that spot measurements outside the residence provide a moderately effective basis for estimating exposure for children living there, but do not perform as well as 24 h stationary measurements in the child's bedroom. Although several other easily-observed variables were associated with personal exposure, they were weak determinants, either individually or in combination.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".