Ionizing radiations epidemiology does not support the LNT model
Bibliographic record
Abstract
Most cancers are multifactorial diseases. Yet, epidemiological modeling of the effect of ionizing radiation (IR) exposures based on the linear no-threshold model at low doses (LNT) has generally not included co-exposure to chemicals, dietary, socio-economic and other risk factors also known to cause the cancers imputed to IR. When so, increased cancer incidences are incorrectly predicted by being solely associated with IR exposures. Moreover, to justify application of the LNT to low doses, high dose-response data, e.g., from the bombing of Hiroshima and Nagasaki, are linearly interpolated to background incidence (which usually has large uncertainty). In order for this interpolation to be correct, it would imply that the biological mechanisms leading to cancer and those that prevent cancer at high doses are exactly the same as at low doses. We show that linear interpolations are incorrect because both the biological and epidemiological evidence for thresholds, or other non-linearities, are more than substantial. We discuss why the LNT model suffers from misspecification errors, multiple testing, and other biases. Moreover, its use by regulatory agencies conflates vague assertions of scientific causation, by conjecturing the LNT, for administrative ease of use.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".