Risk Assessment of Ambient Air Quality by Stochastic-Based Fuzzy Approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A stochastic-based fuzzy risk assessment approach was developed by integrating stochastic simulation, expert involvement, and fuzzy logic within a general framework for systematically examining both the probabilistic and possibilistic uncertainties associated with land cover, environmental guidelines, and health evaluation criteria in an ambient air quality management system. The developed approach was applied to a case study in which sulfur dioxide (SO2) was of interest. Based on the SO2 dispersion modeling results from Monte Carlo simulation, an in-depth fuzzy risk assessment was further employed to quantify the environmental guideline-based risk and health risk due to SO2 inhalation. General risk levels were obtained through fuzzy membership functions and rule bases acquired from a comprehensive questionnaire survey. Scenarios with different air quality guidelines were also analyzed, leading to the variations of risk levels. Results indicated that the developed approach would offer an effective tool for quantifying uncertainties existing in air quality modeling parameters, evaluating their effects in risk levels and providing realistic support to related decision making in air quality management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it