Establishing a Data Fusion Water Resources Risk Map Based on Aggregating Drinking Water Quality and Human Health Risk Indices
Bibliographic record
Abstract
The Drinking Water Quality Index (DWQI) and the Human Health Risk Index (HHRI) are two of the most promising tools for assessing the health impact of water quality on humans. Each of these indices has its own ability to determine a specific level of safety for drinking, and their results may vary. This study aims to develop an aggregated index to identify vulnerable areas in relation to safe drinking water and, subsequently, risk areas for human health, particularly non-cancerous diseases, in the Maku–Bazargan–Poldasht area in NW Iran through the use of a data fusion technique. Nitrate (NO3−) and fluoride (F−) are the predominant contaminants that threaten the local population’s health. The DWQI revealed that the majority of the study sites had poor to improper quality for drinking water class. Health risk assessments showed an excessive potential for non-carcinogenic health risks because of high NO3− and F− exposure through drinking water. Children are at a higher risk for non-carcinogenic changes than adults, according to the total hazard index (THI; NO3− and F−), suggesting that locals have faced a lifetime risk of non-cancer changes as a consequence of their exposure to these pollutants. Using data fusion techniques can assist in developing a comprehensive water resources risk map for decision-making.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".