Using Statistical and Probabilistic Methods to Evaluate Health Risk Assessment: A Case Study
Bibliographic record
Abstract
The toxic chemical and heavy metals within wastewater can cause serious adverse impacts on human health. Health risk assessment (HRA) is an effective tool for supporting decision-making and corrective actions in water quality management. HRA can also help people understand the water quality and quantify the adverse effects of pollutants on human health. Due to the imprecision of data, measurement error and limited available information, uncertainty is inevitable in the HRA process. The purpose of this study is to integrate statistical and probabilistic methods to deal with censored and limited numbers of input data to improve the reliability of the non-cancer HRA of dermal contact exposure to contaminated river water by considering uncertainty. A case study in the Kelligrews River in St. John’s, Canada, was conducted to demonstrate the feasibility and capacity of the proposed approach. Five heavy metals were selected to evaluate the risk level, including arsenic, molybdenum, zinc, uranium and manganese. The results showed that the probability of the total hazard index of dermal exposure exceeding 1 is very low, and there is no obvious evidence of risk in the study area.
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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".