SEATTLE CITY LIGHT SEISMIC RESILIENCY PROGRAM - STRATEGIES, CHALLENGES, AND OPPORTUNITIES
Bibliographic record
Abstract
Earth scientists estimate that in the next fifty years there roughly a one-in-three chance that the worst natural disaster in America -an earthquake with magnitude of 8.0 or higher- will occur off the northwest coast of the country. When the 700-mile subduction zone suddenly releases energy, communities from Canada to California will experience various levels of devastation. Historical records corroborate that many megathrust events have occurred in the past and that the next one is overdue. In addition, as stress continue to build up along the fault line, the risk of such event will continue to increase. During the last decade, West Coast electric utility company Seattle City Light (SCL) has been preparing itself to provide quick rebound following such an event and minimize service disruptions to their nearly one million customers. Strategic actions by SCL include seismic strengthening of old and vulnerable substations, use of control devices and qualified equipment, base isolation of high voltage transformers, installation of dampers on switchyard electric infrastructure, and the implemented modern seismic protection practices both in design and construction. As described in this paper, SCL infrastructure resiliency program is strategic, cost effective and simple. Other utility companies serving in regions of high seismic risk may find SCL knowledge and experience useful to avoid long-term power outages resulting from ground shaking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".