Sont et al. Respond to “Studies of Workers Exposed to Low Doses of Radiation”
Bibliographic record
Abstract
We thank Dr. Gilbert for her thorough commentary (1) on our paper (2). Its many useful comments and its additional tabulation will help put the paper into perspective. In the commentary, Dr. Gilbert focuses on the excess relative risk calculations and identifies various forms of bias. We acknowledge the possibility that our excess relative risks may have been overestimated. We do not suspect a large error from the use of probabilistic linkage, as the methodology was similar to what was used in the National Dose Registry mortality study, where the linkage results were supported by follow-up of vital status (3). Confounding by smoking cannot be assessed in our study because of lack of data. The possibility of confounding by socioeconomic status was considered in the mortality study (3) and was not found to be of concern. The most likely potential source of bias may be the underestimation of workers' doses, which was a consequence of recording most single doses below 0.2 mSv as zero and of the lack of doses before 1951. We are investigating the feasibility of addressing this last issue by using methodology developed at Oak Ridge National Laboratory (4).
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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 | 0.025 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.054 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".