Solar storms and their impacts on power grids Recommendations for (re)insurers
Bibliographic record
Abstract
As shown from past occurrences over the last few decades, solar storms have the potential to impact several human activities (satellites, aviation, power grids, etc.) through various physical phenomena. Coronal mass ejections in particular may generate quasi-DC currents in the bulk power system, causing disruptions which may go as far as the collapse of smaller or larger parts of the power grid, as well as permanent damage to transformers up to the point of failure. Although such situations have already been observed in relatively recent years (notably with the March 1989 power blackout in Quebec), no major solar storms, such as the spectacular 1859 Carrington storm, have been experienced in contemporary times. While there are reasons to believe that a one in 200-year solar storm would not be that different from the 1859 event, studies diverge as to what would be the impact on power grids. Some of them anticipate a major power blackout affecting millions of people people for several weeks or more, with consequences reaching up to trillions of dollars. Others foresee only temporary local outages. Despite these uncertainties and the ‘emerging’ nature of solar storm risk for (re)insurers, it is possible to make a series of recommendations. In particular, in light of the possibility for generating companies and Transmission System Operators (TSOs) to take mitigation measures, (re)insurers should promote such measures, either through reliability standards imposed by grid regulators or when underwriting insurance policies. Special caution is needed when underwriting Contingent Business Interruption (CBI) policies and service interruption extensions due to accumulation risk and sometimes imprecise policy wording.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".