Substitutes, complements, and ripples in multicommodity flows on suspension graphs
Bibliographic record
Abstract
We examine in this article when it is possible to predict, without numerical computation, the direction of change of optimal multicommodity flows on suspension graphs resulting from changes in arc‐commodity parameters. Using results of Evans (Oper Res 26 (1978), 673–679) and of Soun and Truemper (SIAM J Algebr Discrete Meth 1 (1980), 348–358), the multicommodity flow problem on a graph that is two‐isomorphic to a suspension graph is reduced to a single‐commodity flow problem on an enlarged graph, called a “rolodex graph.” Such a reduction allows us to apply results of Granot and Veinott (Math Oper Res 10 (1985), 471–497), developed for single‐commodity network‐flow problems, to derive qualitative sensitivity analysis results for multicommodity flow problems on graphs which are two‐isomorphic to suspension graphs. © 2014 Wiley Periodicals, Inc. NETWORKS, Vol. 64(2), 65–75 2014
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".