Robustness of Ontario power network under systemic risks
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
A failure of one or even a few components would have a limited impact on the network-level performance if the negative consequences are compensated for by neighboring components. However, under certain conditions, this component-level failure may not remain localized, but may rather propagate along other components, thus inducing an entire network/system-level cascade (i.e., systemic) risks. In this respect, the current study develops a simplified model of the Ontario Power Network (OPN) to simulate its topology and load demands. The study then evaluates the different OPN characteristics and develops robustness bands for the OPN under both random failures and targeted threats. Finally, the study presents two dynamic vulnerability indices to facilitate detecting the most critical components within power networks. This study is expected to not only expand the Canadian and the international power network topological analysis database, but also to provide the foundation for innovative network-level systemic robustness enhancement solutions.
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