Study Of Extent of Global Uranium Contamination in Groundwater
Bibliographic record
Abstract
Uranium exposure can result in health risks in both natural and anthropogenic contexts, due to its chemotoxicity and radiotoxicity. The former is anticipated to play a larger role in natural uranium exposure, whilst the latter is more significant in enriched uranium exposure. The largest consumer of groundwater worldwide is India. India is responsible for 85% of the world's freshwater supply and 60% of irrigated agriculture. Uranium is absorbed into the body through contaminated food or uranium-affected water, offering a health danger to humans who may be exposed to high quantities of uranium through their drinking water. The health effects of uranium exposure include leukaemia, prostate cancer, breast cancer, colorectal cancer, lung cancer, kidney cancer, and bladder cancer. Evidence also suggests that drinking water contaminated with uranium might result in chronic renal disease, bone malformations, and liver damage. Uranium in drinking water must not exceed a WHO standard of 30 g/L. Uranium pollution is highest in China, the United States, Germany, Spain, Korea, Myanmar, Mongolia, Burundi, and other nations worldwide. In 151 districts across 18 states in India, high quantities of uranium have been found in ground water. This review focuses on impact of this metal contamination worldwide.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".