Bibliographic record
Abstract
Although almost 120 y of medical experience and data exist on human exposure to ionizing radiation, advisory bodies and regulators claim there are still significant uncertainties about radiation health risks that require extreme precautions be taken. Decades of evidence led to recommendations in the 1920s for protecting radiologists by limiting their daily exposure. These were shown in later studies to decrease both their overall mortality and cancer mortality below those of unexposed groups. In the 1950s, without scientific evidence, the National Academy of Sciences Biological Effects of Atomic Radiation (BEAR) Committee and the NCRP recommended that the linear no-threshold (LNT) model be used to assess the risk of radiation-induced mutations in germ cells and the risk of cancer in somatic cells. This policy change was accepted by the regulators of every country without a thorough review of its basis. Because use of the LNT model has created extreme public fear of radiation, which impairs vital medical applications of low-dose radiation in diagnostics and therapy and blocks nuclear energy projects, it is time to change radiation protection policy back into line with the data.
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 | 0.034 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.050 | 0.049 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".