Commentary on Using LNT for Radiation Protection and Risk Assessment
Bibliographic record
Abstract
An article by Jerome Puskin attempts to justify the continued use of the linear no-threshold (LNT) assumption in radiation protection and risk assessment. In view of the substantial and increasing amount of data that contradicts this assumption; it is difficult to understand the reason for endorsing this unscientific behavior, which severely constrains nuclear energy projects and the use of CT scans in medicine. Many Japanese studies over the past 25 years have shown that low doses and low dose rates of radiation improve health in living organisms including humans. Recent studies on fruit flies have demonstrated that the original basis for the LNT notion is invalid. The Puskin article omits any mention of important reports from UNSCEAR, the NCRP and the French Academies of Science and Medicine, while citing an assessment of the Canadian breast cancer study that manipulated the data to obscure evidence of reduced breast cancer mortality following a low total dose. This commentary provides dose limits that are based on real human data, for both single and chronic radiation exposures.
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 | 0.031 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.068 | 0.098 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".