Critical Infrastructure - Modern Approach and New Developments
Bibliographic record
Abstract
Modern critical infrastructures (CIs) (e.g., electricity, water, transportation, telecommunications, and others) form complex systems with a high degree of interdependencies from one CI to the others. Natural disasters (e.g., earthquakes, floods, droughts, landslides, and wildfires), humanmade disasters (e.g., sabotage and terrorism), and system faults (due to structural and equipment failures) will affect not only the directly impacted CI but all interdependent CIs. Risk assessment, therefore, has to be done over the entire system of CIs and should also include the social and personal impacts. According to a 2022 report, 80% of cities have been affected by significant climate change hazards represented by extreme heat (46%), heavy rainfall (36%), drought (35%), and floods (33%). The impacts of climate change, therefore, affect the complex system of the built environment and result in interrelated consequences at different scales ranging from single buildings to urban spaces and territorial infrastructures. Since it is not possible to reduce the severity of natural hazards, the main opportunity for lowering risk lies in reducing vulnerability and exposure. Vulnerability and exposure are related to urban development choices and practices that weaken the system’s robustness. This volume reviews recent insights from risk identification and reduction to preparedness and financial protection strategies and proposes new approaches for better CIs and built environment protection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".