Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 |
| 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 it