Bibliographic record
Abstract
Many data centers, medical complexes, government facilities, factories and other commercial buildings have standby electrical generators. Quiet for most of the year, the generators are only used in the event of a power outage or for periodic testing. Portland General Electric (PGE) offers a program aimed at supplying capacity resources that utilizes these hidden resources to their full potential as a single aggregated generating peaking plant. PGE's "dispatchable standby generation" (DSG) program currently ties together 15 MWs of standby generators to support peak power demand on PGE's system and to ensure reliable service for customers. This power can also help avoid buying wholesale power when prices are skyrocketing or may allow less expensive resources to be utilized while DSG power can be counted as reserve capacity. DSG generator power first supplies power to its designed load at the facility and any excess power flows into the PGE system. To the PGE system grid, this appears as a drop in load and a small increase in supply. Finally, PGE links the GenOnSys application to PGE's system control center's energy management system via a secure fiber connection.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".