Sensor Node to Improve Resiliency and Monitoring in Smart Grids: Taking the Lab to Field in Industry
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
Sensors and data analytics have a tremendous potential to improve the resilience of the electricity system. Improving the analytics in a smart grid, and providing information to operators so that problems can be resolved quickly, may serve to improve the resiliency of the electricity system. Effective use of sensors and analytics will enable more timely a response, and more efficient use of personnel, specialty equipment, and site location specifics because of better information for deployment teams to address damage. Detection of physical characteristics such as vibration, ice build up, hot spots, aging and deterioration of assets/equipment, metal fatigue and other considerations could prevent disruptions. In cold climates, ice storms can cause outages, and extreme weather events are constantly a threat to electricity towers. A system was developed to collect information from sensors, as well as relevant analytics to detect abnormalities to address damage, and for operator visualizations screens.
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