Statistical methods for analyzing and combining data on low-level exposures to ionizing radiation
Bibliographic record
Abstract
Occupational studies of workers who have been exposed to radiation provide a direct assessment of low-level radiation risks, and can serve as a check on estimates obtained through extrapolation from studies of populations exposed at high levels. Several studies of workers involved in the production of both defense materials and nuclear power in the United States, Great Britain, and Canada are being conducted. If our current risk estimates are correct, these studies have very low power for detecting risks, but can be used to provide useful upper limits on risks. If our current risk estimates are too low, the studies are adequate to detect large departures from these estimates. A broad assessment based on the totality of evidence from all occupational studies is obviously desirable, and such an assessment can be best accomplished by analyzing combined data from all studies. Plans for international combined analyses are underway, and combined analyses on a national scale are also being conducted. In the US, results based on combined data on male workers at the Hanford Site, Oak Ridge National Laboratory (ORNL), and Rocky Flats Weapons Plant have been published, and are used in this presentation to illustrate the application of various statistical procedures. 6 refs.
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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 | 0.156 | 0.351 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".