A GIS-based fuzzy aggregation modeling approach for air pollution risk assessment
Bibliographic record
Abstract
In this study, a fuzzy aggregation modeling approach was developed for the cumulative risk assessment associated with multiple air pollution factors and evaluation criteria in a GIS-based air quality management system. This is based on the fact that to assess the cumulative risk from multiple air pollution factors is very difficult due to the existence of various uncertainties and complexities. Fuzzy membership functions were then used to quantify these uncertainties and complexities. In addition, an ordered-weighted product (OWP) approach was used to identify the relative importance of each pollution factor. A number of tasks have been undertaken, including (1) quantification of the evaluation criteria using fuzzy sets; (2) construction of fuzzy membership functions; (3) calculation of the relative importance, i.e. weighting coefficient wifor each air pollution factor; (4) construction of fuzzy aggregation-OWP modeling; (5) assessment of the air pollution cumulative risk. The developed approach was applied to a case study of California. Reasonable results have been generated, which are useful for evaluation of the cumulative risk resulting from multiple air pollution factors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".