Generating maximal efficient faces for the multiobjective multicommodity flow problem
Why this work is in the frame
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Bibliographic record
Abstract
Multicommodity flow problems (MCFPs) arise when several commodities are to be transmitted within a capacitated network. MCFP has received a great attention in the literature for the single objective case, while only few works addressed the problem in a multiobjective framework. In this paper, we study the MCFP with multiple objectives. This problem is modeled as multiobjective linear program with continuous decision variables. In order to solve this problem, we propose to apply an exact solution approach operating in the objective space, called the Efficient Solutions Adjacency based Method (ESAM) to generate all the maximal efficient faces and extreme points. An experimental study is conducted to test the efficiency of the ESAM on solving small and medium sized multiobjective MCFPs.
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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