Bibliographic record
Abstract
In his letter, Puskin (2011) criticizes ecological measurements for radon concentration that were used in my analyses (Hart, 2011a, b) but then cites a study (Turner et al, 2011) to support his view that also used ecological measurements for radon estimates. In the other study that Puskin cites, namely Darby et al, 2005, it is unclear how the radon measurements were obtained. Indeed one peculiar statement is found in this latter citation: “For homes where radon measurements were unobtainable, we estimated the concentration from measurements in the homes of controls” [emphasis added] (Darby, 2005). Case control and ecological studies on radon share a common weakness. Rather than determining actual individual absorptions, both designs provide estimates of population exposures, though case control estimates are purportedly more individualized (e.g., measurements taken from individual homes). On the other hand, direct evidence, in the form of actual individual absorptions, along with corresponding clinical findings, is available for those who are interested. Radium, which decays by alpha emission to radon (U.S. EPA, 2011a), has been found to have a relatively large margin of safety. This margin of safety was found in a study of approximately 500 persons who had skeletal mean doses of < 1000 cumulative rads, yet they had “no signs or symptoms of clinically significant radiogenic effects” (Evans, 1974). Puskin also notes the confounding effect that smoking can have on data analysis. However, my comments (Hart, 2011a, b) were made in regard to “deadly radon” (Schontzler, 2010) rather than “deadly radon when confounded by smoking.” Indeed, the U.S. EPA states, without qualifying the confounding effect of smoking in the statement, that “overall, radon is the second leading cause of lung cancer” (U.S. EPA, 2011b).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".