Interval Recourse Linear Programming for Resources and Environmental Systems Management under Uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
An interval recourse linear programming (IRLP) approach is proposed in this study for mitigating constraint violation problems in resources and environmental systems management (REM) under interval uncertainties. Based on a review of interval linear programming (ILP) and its significances to REM, two linear programming sub-models are employed to initialize a decision space for IRLP. Causes of constraint violation are examined based on identification of a violation criterion. Contraction ratios are defined after revelation of violation ranges of constraints. As a recourse measure to constraint violation problems, another two linear programming sub-models are constructed given a series of contraction ratios. A hypercube decision space where infeasible solutions are excluded is obtained. Post-optimality analysis is conducted to deal with barriers for applying the IRLP approach to real-world ILP models for REM. An REM problem is introduced to demonstrate procedures and effectiveness of the IRLP approach. Comparisons with existing ILP methods reveal that the IRLP approach is effective at resolving the constraint-violation problem, reproducing the largest decision space which does not include infeasible solutions, and enhancing reliability of decision support for REM.
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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