IMPACT OF FLUORIDE TOXICITY- A WORLDWIDE THREAT
Bibliographic record
Abstract
Fluoridation of water is a significant issue globally since millions of people utilise groundwater for drinking, causing health problems such dental and skeletal fluorosis as well as numerous other health hazards. Earlier studies were restricted to local or regional scales. The World Health Organization (WHO) states that 1.5 mg/l of fluoride is the safe limit for groundwater. Globally, Pakistan, China, and India are the most severely impacted nations. Fluoride pollution can induce osteosarcoma, dental fluorosis, and skeletal fluorosis, among other conditions. Since the issue has significant socioeconomic implications as well, there is a need for a serious concern in managing the fluoride level in ground water from a worldwide perspective. The investigation will assess more than a century's worth of studies conducted globally on the effects of fluoride pollution on human health. The researchers looked at the sources, mobilisation, association, and spread of fluoride contamination in water; these topics are covered here. Although research has been done all over the world, especially in developing nations like India, one area that is still urgently in need of attention is the cleanup of this contamination in ground water supplies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".