Impact Of Chronic Arsenic Toxicity on Human Health- A Review
Bibliographic record
Abstract
The levels of heavy metals in ground water continue to be higher than those considered acceptable by regulatory agencies in different countries across the world. One of the most important public health problems in the world is chronic arsenic poisoning, or arsenicosis, caused by drinking water that has been poisoned with arsenic. Arsenic poisoning over time has been related to a number of cancers of the skin, oral cavity, urinary bladder, kidney, and lung in addition to bone marrow depression, Blackfoot disease, cardiovascular disease, diabetes, hypertension, and a host of other ailments. In addition, arsenic causes DNA damage that has genotoxic effects. Around the world, 137 million people in 70 different nations depend on drinking water that has been drawn from severely contaminated groundwater. The two nations that have been affected the most so far are Bangladesh and West Bengal, India. The drinking water of 26 million people in nine districts of West Bengal contains levels of arsenic that are significantly higher than the WHO-acceptable limit of 10 g/l. The review focuses on the impact of chronic arsenic toxicity on public health 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".