An Analysis of Cancer causing Substances and its Impact on Prevalence of Cancer Cases among General Population residing along Thamirabharani River Basin – Evidence based research
Bibliographic record
Abstract
As water quality degradation worsens in many countries, economic growth is stunted, health conditions worsen, food production is reduced, and poverty is exacerbated. The aim is to study the quantified levels of carcinogens in the environment, according to IARC Classification such as Arsenic, Cadmium, Chromium, Nickel, Lead, Nitrite/Nitrate and Phosphates and its correlation with spurts of cancer cases across certain sites that lie along Thamirabharani river basin, Tamil Nadu, India. A Systematic literature review for cross-sectional studies that provided information about the groundwater quality was carried out using six databases, "Researchgate, PubMed, Elsevier science direct, Wiley Online Library, Medline and Springerlink" from 2005 to 2021. TNCRP 2021 was taken as a reference to analyze the percentage of all types of cancer cases reported in the districts that lie along the river basin. Quality assessment was done using Newcastle Ottawa Scale. The results show the presence of high concentration of carcinogens in the groundwater and sediments collected near the industries, agricultural land and municipal sewage yard. The significant association between exceeding of the permissible limit of various heavy metal elements and compounds and percentage of cancer cases at that particular site is established in this study. Oral findings commonly associated with chronic toxicity of carcinogenic heavy metals are listed which serves as an alarming signal to adapt to healthy lifestyle and dietary modifications as required.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".