How to keep the lights on: Lessons from major blackouts over the last 35 years [Editors’ voice]
Bibliographic record
Abstract
At the time of this writing, in January 2023, Québec is remembering the 1998 ice storm, which occurred on Friday, 9 January, 34 years ago. Falls of more than 100 mm of freezing rain literally pulverized part of the Hydro-Québec electrical network, blacking out more than 4 million people. This day has gone down in history asBlack Friday. About 24,000 wooden poles, 900 pylons, and 3,000 km of power lines were cut down. Some households would only find power after five to six weeks (Figure 1). Such a weather-driven blackout was not the first to hit Québec province. On 13 March 1989, solar winds disrupted the Earth’s magnetic field, causing seven static var compensators (SVC) to disconnect from the grid in 1 min, due to excessive harmonics currents. Following the loss of the dynamic voltage support provided by the SVCs, the instability of the power system became inevitable. A major outage of the entire Québec network resulted, leaving 6 million people without power for 9–12 h.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 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".