Terminal reliability of road networks with multiple destination options
Bibliographic record
Abstract
In crisis situations such as natural disasters, it is essential that people can be reached by fire and rescue services as well as police forces.Moreover, access to food and water supply should be ensured for everyone, for instance.In all cases, people depend on the (potentially damaged) road infrastructuremodelled as a graph network -that for all nodes in the network should provide connections to at least one supply node of each type (i.e., fire department, hospital etc.) even if some links in the network are currently unavailable.Assuming that there are multiple supply nodes per type among the whole network, the present contribution discusses the risk that a given node of the network becomes isolated from all of these supply nodes depending on the topological structure of the network.For this purpose, the well-known concept of terminal reliability is adapted to the situation with multiple possible destinations which is realized by appropriately modifying the original graph.An algorithm is presented that allows finding all relevant cut sets in the modified graph which can be used for computing the probability R sys that a given node remains connected to at least one of the supply nodes considered depending on the link failure probabilities p i .A simple clustering approach together with Boolean algebra finally yields explicit numbers for R sys depending on p i .The whole concept is demonstrated based on an illustrative example showing the different endangerment among the nodes of the network including a discussion about the specific criticality of each network link with regard to ensuring the connectivity between supply nodes and other nodes.Thus, by identifying critical links and quantifying the risk of isolation for all nodes in the network, the present contribution provides useful tools for prevention in crisis management.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".