Critical Flow Centrality Measures on Interdependent Networks with Time-Varying Demands
Bibliographic record
Abstract
This paper describes a novel method for allowing urban planners and municipal engineers to identify critical components of interdependent infrastructure networks whose attributes vary over time. The method is based on critical flow analysis, wherein system components are ranked by their role in facilitating the flow of resources to critical locations. The intent of the method is to support decision making by providing a means by which stakeholders can reason about the way in which changes in supply, demand, or network capacity can alter the distribution of critical flows within an urban environment. Individual infrastructure systems are modeled as networks that can be linked to one another by physical and geospatial dependencies. A simple instantiation of the method is presented and evaluated on a district-scale model of a city that contains water and electricity networks. The paper also discusses two forms of reliability analysis based on critical flows: a composite measure incorporating edge reliability, and a variation on standard component failure/degradation analysis.
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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 | 0.000 | 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 | 0.001 |
| 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".