Introduction to the U.S. Million Person Study of health effects from low-level exposure to radiation
Bibliographic record
Abstract
The epidemiologic study of one million U.S. radiation workers and veterans on health effects following low-level radiation exposure (Boice, Cohen, et al. 2019), or the Million Person Study (MPS 1 ), has been underway in some form for more than a quarter of a century. The MPS was designed to examine health effects after chronic exposure to low dose-rates of radiation, in contrast to the brief exposure at a high dose-rate experienced by the Japanese atomic bomb survivors. The study will provide important scientific evidence needed for sound radiation protection policy and recommendations (NCRP 2018a; Boice, Held, et al. 2019). This special issue consists of 26 articles, including this introduction and an editorial (Wakeford 2021). The aim for this special issue is to present a comprehensive overview of the MPS with regard to: its conceptual development and historical perspectives, methodological approaches for both epidemiology and dosimetry, the first publications of quantitative results to date, as well as a summary of the first international virtual symposium with key stakeholders and researchers on the MPS that also casts a vision for the future.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.041 | 0.022 |
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".