Bibliographic record
Abstract
Dear Sir Whilst I appreciate the concerns that Barrie Skelcher may have with regard to the healthy worker effect (HWE), may I assure him that it is not fundamental to understanding the risks from radiation exposure. Not only is the HWE much quoted, it is a much studied and multifaceted phenomenon in occupational epidemiology. It is a bias induced by comparing the worker population with the national population, often resulting in lower than expected SMRs and/or SRRs, and is not peculiar to occupational studies of nuclear workers. As these comparisons are known to be subject to bias, the SMRs and/or SRRs must be interpreted with caution. The evidence for a statistical association between health outcome and exposure arises, however, out of the trend tests. These tests are `internal', i.e. they are not dependent on any external population and hence are unaffected by the HWE. If Barrie accepts the Popperian philosophy that hypotheses cannot be proved right but can only be discredited, then, to cast doubts on the utility of the linear non-threshold (LNT) hypothesis it is necessary to show that either the dose response is non-linear at low doses and/or that there is a threshold below which there is no dose response. Because radiation-induced diseases do not leave a `marker' to distinguish them from non-radiation induced diseases, epidemiological studies are unlikely to invalidate the LNT hypothesis in the foreseeable future. Barrie may note that this argument does not depend on the existence of the HWE. If a study does not find any detrimental effects on health resulting from radiation exposure, the HWE cannot therefore be `wheeled out' to explain this so called anomaly with the LNT hypothesis. The reasons that some studies are unable to demonstrate a dose response is more to do with the power of the study. The power of the study is dependent on such factors as the number of participants in the study, the number of years that they have been followed up for and, of course, the exposures that the participants have encountered. Yours faithfully,
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.088 | 0.010 |
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".