Bibliographic record
Abstract
Currently, several epidemiologic studies of workers who have been exposed occupationally to low levels of radiation are being conducted, and include studies of workers in the United States, Great Britain, and Canada involved in the production of both defense materials and nuclear power. This paper focuses on studies that evaluate the possible adverse effects resulting from external exposure to radiation. The radiation risk estimates that have been used to establish radiation protection standards for workers and others have been obtained mainly from studies of persons exposed at high doses (generally exceeding 100 rem) and dose rates. The study of Japanese atomic bomb survivors in Hiroshima and Nagasaki is by far the most important study, but, in addition, there are many studies of persons exposed for medical reasons. However, questions remain with regard to the extrapolation process that has been necessary for estimating low-level radiation risks. Occupational studies provide a direct assessment of risk based on data on persons exposed at the actual levels of interest. If current risk estimates are correct, these studies have very little chance of detecting risk, but can still be used to provide useful upper limits on risks. The studies are also adequate to detect serious underestimation of risks.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".