Fuzzy‐based Integrated Risk Assessment of Methylmercury in Lake Phewa, Nepal
Bibliographic record
Abstract
Mercury (Hg) can enter a lake via processes such as air deposition and run-off. In lake water, Hg is converted into highly neurotoxic methylmercury (MeHg), primarily by bacteria, and the MeHg is then bioaccumulated and biomagnified along the food chain. The data and models used in risk assessment may be associated with uncertainty that can be incorporated into the analysis using a fuzzy approach. The objective of this research was to assess the integrated human health and ecological risks of MeHg in Lake Phewa, Nepal using a fuzzy approach. The concentration of MeHg in fish tissue was obtained by sampling fishes and their laboratory analysis using standard methods. The measured MeHg in fish tissue was used to estimate the concentration of MeHg in water by applying a bioaccumulation factor (BAF). MeHg concentration in rice was also estimated using data obtained from the literature. A questionnaire survey was conducted to estimate fish consumption rates among different occupations: fishermen, local people, hotel owners, government staff, army/police, and others (visitors). An integrated risk assessment (IRA) framework proposed by the World Health Organization (WHO) was applied to estimate health risk to humans and ecological risk to fishes. The results show that fish consumption contributed approximately 90% or higher to overall human health risk and the remaining risk was attributed to rice consumption. Hotel owners, fishermen, and others (visitors) in particular had potentially significant health risks from MeHg, with hotel owners being an even higher risk group. The higher health risk was primarily due to very high fish consumption rates. Moreover, the ecological risk to fishes was within acceptable levels. The risk estimate using the fuzzy approach approximates uncertainty in decision-making, building more confidence in decisions for risk assessors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".