A Look at the Grouping Effect on Population-level Risk Assessment of Radon-Induced Lung Cancer
Bibliographic record
Abstract
On the basis of considerable knowledge gained by studying health effects in uranium and other underground miners who worked in radon-rich environments, radon exposure has been identified as a cause of lung cancer. Recent pooled analyses of residential studies have shown that radon poses a similar risk of causing lung cancer in the general public when exposure occurs at generally lower levels found in homes. With the increasing accessibility of statistical data via the internet, people are performing their own analyses and asking why, in some cases, the lung cancer occurrence at the community level does not correlate to the radon levels. This study uses statistical data available to the general public from official websites and performs simple analyses. The results clearly show the difficulty in linking observed lung cancer incidence rates at the provincial/territorial level, with possible cause, such as smoking or radon exposure. Even the effect of smoking, a well-documented cause of lung cancer, can be overlooked or misinterpreted if the data being investigated is too general (i.e., summary data at population level) or is influenced by other factors. These difficulties with simple comparisons are one of the main reasons that epidemiological studies of lung cancer incidence and radon exposure requires the use of cohorts or case controls at the individual level as opposed to the more easily performed ecological studies at the population level.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".