Uranium Mining and Norm in North America—Some Perspectives on Occupational Radiation Exposure
Bibliographic record
Abstract
All soils and rocks contain naturally occurring radioactive materials (NORM). Many ores and raw materials contain relatively elevated levels of natural radionuclides, and processing such materials can further increase the concentrations of naturally occurring radionuclides. In the U.S., these materials are sometimes referred to as technologically-enhanced naturally occurring radioactive materials (TENORM). Examples of NORM minerals include uranium ores, monazite (a source of rare earth minerals), and phosphate rock used to produce phosphate fertilizer. The processing of these materials has the potential to result in above-background radiation exposure to workers. Following a brief review of the sources and potential for worker exposure from NORM in these varied industries, this paper will then present an overview of uranium mining and recovery in North America, including discussion on the mining methods currently being used for both conventional (underground, open pit) and in situ leach (ISL), also referred to as In Situ Recovery (ISR), and the production of NORM materials and wastes associated with these uranium recovery methods. The radiological composition of the NORM products and wastes produced and recent data on radiological exposures received by workers in the North American uranium recovery industry are then described. The paper also identifies the responsible government agencies in the U.S. and Canada assigned the authority to regulate and control occupational exposure from these NORM materials.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".