Bibliographic record
Abstract
Many real-life applications, arising in transportation and telecommunication systems, can be mathematically represented as shortest path problems. The deterministic version of the problem, where a deterministic cost is associated to each arc and the configuration of the network (nodes and arcs) is assumed to be known in advance, is easy to solve and has been extensively studied. However, in real applications, costs are typically not known a priori and may be subject to significant uncertainty. In addition, due to failure, maintenance, natural disasters, weather conditions, etc., some arcs could not be available causing a change of the network configuration. In this paper we introduce a variant of the shortest path problem under uncertainty, that concerns the situation in which for each arc two different states are possible (i.e. operating and failed states) and the aim is to find the path connecting a given pair of nodes with a sufficiently large probability α and such that the total cost is minimized. The problem can be formulated as a large scale integer programming model with knapsack constraints. For its solution a heuristic approach has been designed and implemented. Preliminary numerical experiments have been carried out on a set of randomly generated test problems.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".