Mitigating blackout along the cascading pathways
Bibliographic record
Abstract
Many countries experienced severe power outages caused by ice/wet-snow storms in recent years. Affected by climate change, scientists believe that similar disaster will be more frequent, more severe and longer lasting. Most of our present prevention and treatment methods are not fully developed for wide practical application. Building a power network that can tolerate any snow storm, will be too costly to be feasible. In this paper, based on investigation of Vancouver's snow-caused power outage in Nov 2006, a dependency network has been built to represent these principal cascading pathways unfolded during this power outage, with which the effects of changed climate, the causalities among these root conditions and consequences etc. are illustrated, the developing mechanism of such kind of disaster is pinpointed accordingly. Through analysis on this network, we propose a systematic strategy to actively suppress the development of snow storm, as well as to block its propagation along these cascading pathways. Related countermeasures, such as to build an early warning system, to inhibit the development of a wet-snow disaster by artificial precipitation stimulation, to strengthen these vulnerable locations with underground cable etc., are described.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".