Perceiving Critical Infrastructure with A New Awareness of Cyber Risk
Bibliographic record
Abstract
North America’s critical infrastructure has been the subject of cyber-attack, in various cycles of activity, for many years. In March of 2017, a cyber-attack caused periodic ‘‘blind-spots” for electricity distribution grid operators in the Western US for about 10 dangerous hours. In May of this year, there was panic at the gas pumps across many States in southeastern United States, which has been attributed to a cyber-attack on a major US pipeline that disrupted fuel supplies to the US East coast. US Commerce Secretary Raimondo soon after that attack announced that those sorts of attacks are becoming more frequent and that combating such attacks against critical infrastructure is a ‘‘top priority” of the Biden Administration. At home, the Canadian Center for Cyber Security’s 2020 Report on ‘‘National Cyber Threat Assessment” warned that foreign provocateurs ‘‘are very likely attempting to develop cyber capabilities to disrupt Canadian critical infrastructure.” On March 11, 2021, the Trade Commissioner Service of Canada stated, in its report ‘‘Spotlight on Cybersecurity,” that ‘‘Attacks on critical infrastructure have become a growing cause of concern for governments and provide sector providers around the world. . .the increase of inter-facing networks has led to an increase in the number of cyber-attacks (on those) infrastructures.”
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".