TESTING A BEIR-VI SUGGESTION FOR EXPLAINING THE LUNG CANCER vs. RADON RELATIONSHIP FOR U.S. COUNTIES
Bibliographic record
Abstract
The BEIR-VI Report suggests that the large discrepancy between the observed lung cancer rate vs. radon exposure relationship for U.S. counties, and the predictions of linear no-threshold theory, may be explained by a strong negative correlation between smoking intensity and radon exposure. It proposes a model for testing that suggestion. We apply that model to the detailed data for U.S. counties; analysis shows that even a perfect negative correlation explains little more than half of the discrepancy, and the largest not-implausible correlation can explain less than a quarter of the discrepancy. We then extend the BEIR-VI suggestion to include a strong negative correlation between both the prevalence of smoking and the intensity of smoking. The largest not-implausible correlations can explain no more than 30% of the discrepancy. It is concluded that the previous interpretation of these data, that linear no-threshold theory fails this test, is sustained.
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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 | 0.028 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".