A coupled factorial-analysis-based interval programming approach and its application to air quality management
Bibliographic record
Abstract
UNLABELLED: In this study, a coupled factorial-analysis-based interval programming (CFA-IP) approach is developed through incorporating factorial analysis within an interval-parameter linear programming framework. CFA-IP can tackle uncertainties presented as intervals that exist in the objective function and the left- and right-hand sides of constraints, as well as robustly reflect interval information in the solutions for the objective-function value and decision variables. Moreover CFA-IP has the advantage of investigating the potential interactions among input parameters and their influences on lower- and upper-bound solutions, which is meaningful for supporting an in-depth analysis of uncertainty. A regional air quality management problem is studied to demonstrate applicability of the proposed CFA-IP approach. The results indicate that useful solutions have been generated for planning the air quality management practices. They can help decision makers identify desired pollution mitigation strategies with minimized total cost and maximized environmental efficiency, as well as screen out dominant parameters and explore the valuable information that may be veiled beneath their interrelationships. IMPLICATIONS: The CFA-IP approach can not only tackle uncertainties presented as intervals that exist in the objective function and the left- and right-hand sides of constraints, but also investigate their interactive effects on model outputs, which is meaningful for supporting an in-depth analysis of uncertainty. Thus CFA-IP would be applicable to air quality management problems under uncertainty. The results obtained from CFA-IP can help decision makers identify desired pollution mitigation strategies, as well as investigate the potential interactions among factors and analyze their consequent effects on modeling results.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".