An Open-Data-Based Methodology for the Creation of a Graph of Critical Infrastructure Dependencies at an Urban Scale
Bibliographic record
Abstract
This paper presents the MARIS (Modeling infrAstructuRe dependencIes at an urban Scale) methodology, allowing the generalization of one of the possible graphs modeling Critical Infrastructure (CI, hereafter) interdependencies at an urban scale starting from uncertain data. This leverages a set of known interdependencies at the system level, topological open data of local services and Points of Interest collected at an urban scale, and some heuristics. Indeed, interdependencies at an urban scale are usually not known to decision makers (e.g., CI operators, emergency planners) due to, for example, a lack of integration of knowledge held by different critical infrastructure operators and privacy restrictions. Here, these interdependencies are determined through geographic-based strategies. The resulting graph can be a valuable input to simulate emergency scenarios of CIs in the area of interest and, thus, plan proper countermeasures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".