Issues affecting uranium mining in the 21st century
Bibliographic record
Abstract
Immediately after World War 2 following the nuclear bombing of Nagasaki and Hiroshima in Japan by the United States of America (USA), there was a race between the Union of Soviet Socialist Republics (USSR), China, the United Kingdom (UK), France, India, Pakistan, Israel and South Africa to develop nuclear weapons as had been achieved by the USA via the Manhattan Project. This resulted in the USSR developing a gulag named Wismut at Erzgebirge (Ore Mountains) in the south-east of the recently annexed East Germany. Similarly, the USA began mining high grade uranium ore using convict labour in Colorado. France and Czechoslovakia (also annexed by the USSR) followed suit, as did Canada and Australia who willingly supplied both the USA and the UK in the 1960s and 1970s with uranium for nuclear weapons. Nobody had any idea about the consequences to uranium miners’ health at the time. The major issue is lung cancer due to radon gas exposure which has a 25 to 30 year gestation period. Kelly-Reif (2023), a USA epidemiologist, has recently reported the current status of lung cancer deaths from seven cohorts in the USA, Canada, Germany, France and the Czech Republic. The total is 7,754 lung cancer deaths. This figure does not include South Africa, Australia, China, India, Pakistan and Niger where very significant uranium mining has also taken place. In Australia, there has been negligible follow-up of uranium miners’ health which is why it was not included in the PUMA study.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".