Data management issues associated with the August 14, 2003 blackout investigation
Bibliographic record
Abstract
The largest blackout in the history of the North American electric power grid occurred on August 14, 2003. An extensive investigation into what happened (and why) began immediately. The joint U.S.-Canadian task force led the effort, including support from the electric utility industry and several federal agencies, e.g. the U.S. Department of Energy. The North American Electric Reliability Council (NERC) supported the task force, including particularly the electricity working group. The overall blackout investigation team drew expertise from a large number of organizations, assembled into teams to address specific attributes of the blackout. This work describes the data management issues associated with supporting the blackout investigation, beginning with the immediate response in the days and weeks following the blackout, supporting the interim report, to the long-term plans for deriving lessons learned for implementing improvements in the overall process of outage disturbance reporting. The sole focus of This work is the electricity working group activities at NERC; the security and nuclear working groups are outside the scope of this paper.
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.
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 | 0.097 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".