ENERGY INFRASTRUCTURE AND SECURITY
Bibliographic record
Abstract
▪ Abstract Concerns about safeguarding key infrastructures (such as energy, communications, banking, and roads) from deliberate attack are long-standing, but since the end to the cold war, emphasis has turned to the possible impacts of terrorism. Activities to address these concerns are sometimes called critical infrastructure protection (CIP), a concept that is somewhat different from the one of “energy security,” which focuses on politically and economically motivated supply interruptions. Different elements of the energy infrastructure are characterized by distinct vulnerabilities. Breaches of security in nuclear plants can lead to large-scale environmental disasters—but the infrastructure is concentrated and relatively easy to guard. Oil and gas production, transportation, and refining infrastructures are often spatially concentrated, and disruptions can lead to shortages if supply is not restored before stockpiles are exhausted. Traditional electricity infrastructures suffer from the need for system-wide integrity to ensure supply reliability, having critical facilities spatially concentrated (substations), and insignificant storage capacity for emergency supply. This review discusses how energy infrastructure and security are related, how this relationship differs from traditional energy security concepts, and what it may mean for private and policy decisions. Key concepts include redundancy, diversity, resilience, storage, decentralization, and interdependence. The concept of CIP is still relatively new and is likely to evolve over time, possibly away from a “guards, gates, and guns” defensive approach and toward a design approach that yields systems that are inherently harder to successfully attack. Such survivable systems may feature distributed intelligence, control, and operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".