A real-time investigation on public health consequences of chromium laden effluent from the leather industries
Bibliographic record
Abstract
Background: Extensive evidence of elevated chromium (Cr) in the surface and groundwater exists. However, the importance of exposure toward Cr-associated health risks in areas with tanneries is still often neglected. These situations prompted an independent research study to investigate the views of those close-knit communities who are being directly affected by this tannery village. Objectives: To establish an understanding of the local demographics and; exploratory data analysis (EDA) to discover patterns in the occurrence of diseases; measure the awareness of residents of the presence of Cr and its harmful health effects. Methods: The EDA technique was used to conduct survey studies on data to uncover patterns, identify anomalies, test hypotheses, and validate assumptions using summary statistics and graphical representations. Results: Out of 14 diseases directly linked to high Cr toxicity, the study reported high levels of diarrhea, epigastric pain, and moderate-to-severe cases of vertigo among those aged more than 22 years. With over 90 active tanneries and toxic Cr released to the environment in a town of <78 km2 area (1.2 tannery/km), there are only 15 hospitals (0.2 hospitals/km) for the population currently accommodating more than a quarter-million people daily (3205 people/km). Conclusion: New mitigation strategies must be put forth to alleviate the negative impacts of the tanneries on the residents and nature most affected as well as the population as a whole.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".