Radiological protection in North American naturally occurring radioactive material industries
Bibliographic record
Abstract
All soils and rocks contain naturally occurring radioactive material (NORM). Many ores and raw materials contain relatively high levels of natural radionuclides, and processing such materials can further increase the concentrations of natural radionuclides, sometimes referred to as 'technologically enhanced naturally occurring radioactive material' (TENORM). Examples of NORM minerals include uranium ores, monazite (a source of rare earth minerals), and phosphate rock used to produce phosphate fertiliser. Such activities have the potential to result in above background radiation exposure to workers and the public. The objective of this paper is to review the sources and exposure from NORM in North American industries, and provide a perspective on the potential radiological hazards to workers and the environment. Proper consideration of NORM issues is important and needs to be integrated in the assessment of these projects. Concerns over radioactivity and radiation amongst non-governmental organisations and the local public have resulted in the cancellation of NORM mining and mineral extraction projects, as well as inhibition of the safe use of by-product materials from various NORM industries. This paper also briefly comments on the current regulatory framework for NORM (TENORM) in Canada and the USA, as well as the potential implications of the recent activities of the International Commission on Radiological Protection for NORM industries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 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".