SEATTLE CITY LIGHT SEISMIC RESILIENCY PROGRAM - STRATEGIES, CHALLENGES, AND OPPORTUNITIES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it