Bibliographic record
Abstract
In 2009, the International Commission on Radiological Protection issued a statement on radon which stated that the dose conversion factor for radon progeny would likely double, and the calculation of risk from radon should move to a dosimetric approach, rather than the longstanding epidemiological approach. Through the World Nuclear Association, whose members represent over 90% of the world's uranium production, industry has been examining this issue with a goal of offering expertise and knowledge to assist with the practical implementation of these evolutionary changes to evaluating the risk from radon progeny. Industry supports the continuing use of the most current epidemiological data as a basis for risk calculation, but believes that further examination of these results is needed to better understand the level of conservatism in the potential epidemiological-based risk models. With regard to adoption of the dosimetric approach, industry believes that further work is needed before this is a practical option. In particular, this work should include a clear demonstration of the validation of the dosimetric model which includes how smoking is handled, the establishment of a practical measurement protocol, and the collection of relevant data for modern workplaces. Industry is actively working to address the latter two items.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.023 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".