An approach to identifying geographic interdependencies among critical infrastructures
Bibliographic record
Abstract
Interdependencies among Critical Infrastructures (CIs) are the basis for domino effects that may have serious consequences for society. Especially in urban areas, the high density of these infrastructures and their geographic proximity may result in failures due to geographic interdependencies, one of the four types of interdependencies that exist among CIs. Studying the issue of geographic interdependencies raises a specific problem that must be addressed. Such a study necessarily requires information on the location of system infrastructures in order to determine their proximity. Access to this kind of information is one of the major difficulties associated with the study of geographic interdependencies. Because this kind of information is confidential, systems are not willing to share it. This article presents an approach that makes it possible to study geographic interdependencies among CIs but requires only a minimum of information on the specific location of system infrastructures. Based on the concept of so-called flexible cartography, this approach provides relevant results for the processing of interdependencies, while respecting organisations' confidentiality constraints.
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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 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".