The Economic Impact of Critical National Infrastructure Failure Due to\n Space Weather
Bibliographic record
Abstract
Space weather is a collective term for different solar or space phenomena\nthat can detrimentally affect technology. However, current understanding of\nspace weather hazards is still relatively embryonic in comparison to\nterrestrial natural hazards such as hurricanes or earthquakes. Indeed, certain\ntypes of space weather such as large Coronal Mass Ejections (CMEs) are an\narchetypal example of a low probability, high severity hazard. Few major\nevents, short time-series data and a lack of consensus regarding the potential\nimpacts on critical infrastructure have hampered the economic impact assessment\nof space weather. Yet, space weather has the potential to disrupt a wide range\nof Critical National Infrastructure (CNI) systems including electricity\ntransmission, satellite communications and positioning, aviation and rail\ntransportation. Recently there has been growing interest in these potential\neconomic and societal impacts. Estimates range from millions of dollars of\nequipment damage from the Quebec 1989 event, to some analysts reporting\nbillions of lost dollars in the wider economy from potential future disaster\nscenarios. Hence, this provides motivation for this article which tracks the\norigin and development of the socio-economic evaluation of space weather, from\n1989 to 2017, and articulates future research directions for the field.\n
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".