Interval-Based Air Quality Index Optimization Model for Regional Environmental Management Under Uncertainty
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
In this study, an interval-based air quality index optimization (IAQO) method was developed for the planning of regional air quality management systems. The developed IAQO method introduced an air quality index (AQI) concept into an interval mathematical programming (IMP) framework to handle uncertainties expressed as interval values in the model's left- and right-hand sides and objective function over a multipollutant environmental management for its capacity of integrated evaluation and health risk analysis with ambient concentrations. A management problem for controlling total air pollutant concentrations was studied to illustrate applicability of the proposed IAQO approach. A number of scenarios based on different ambient air quality management policies were analyzed. Results indicate that reasonable solutions have been generated under different levels of violating AQI risk. They can help decision makers to identify desired alternatives for mitigating air pollution with cost minimization and for providing services for regional air quality management decisions.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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