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
Power system benchmarks are transmission and distribution networks used to evaluate novel control algorithms and simulate grid evolution scenarios. These benchmarks range in size, system characteristics, and use cases. Although active working groups have created and published many benchmarks, these networks are not all representative of a given region and may not consider certain aspects such as increased penetration levels of distributed energy resources. To address these issues, synthetic benchmark networks and methodologies for generating them have been developed by various research groups. This paper provides a comprehensive survey of procedures commonly used to generate synthetic networks and a detailed account of the various metrics used to define and validate benchmarks. Existing models are categorized into different approaches, including expert design, anonymized clustering, statistical sampling, and heuristic algorithms. Deep graph generation based techniques are also presented and recommended for the network generation problem. A comparative summary is provided to highlight the different existing works in this area and reveal research gaps, along with a list of published datasets and their characteristics.
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