Efficient management of air quality considering fuzzy confidences with varied reliabilities
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
Regional air quality management systems are complicated by uncertainties due to their interactive, dynamic, and multi-objective features. In this study, an inexact double-sided fuzzy chance-constrained programming (IDFCCP) model was developed and applied to a hypothetical case of regional air quality management. The results indicated that the proposed IDFCCP improved upon the existing ILP and DFCCP approaches; the fuzzy confidences at different levels could be analysed with varied reliability scenarios, making it possible to handle fuzzy uncertainties originating from both sides of the model constraints; other uncertain parameters could be expressed in terms of discrete intervals. The trade-off between system economy and reliability could be analysed by decision makers according to their preferences. The study results demonstrated that the proposed method could help decision makers identify desired policies under various environmental, economic, system-feasibility and system-reliability constraints.
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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