Bibliographic record
Abstract
The escalating threat of wildfires has been inflicting growing harm on power systems in recent years. Transmission lines, especially those supported by wooden poles, are susceptible to destruction by wildfires, leading to loss of load (loss of energy). It is, therefore, crucial to incorporate the unavailability of transmission lines due to wildfire into the quantitative risk assessment of power systems.Burn-P3 (probability, prediction, and planning) is a spatial fire simulation model that is commonly used in land-management planning and wildfire research. Its application can be extended to simulate the ignition and spread of wildfires in transmission corridors. The Burn-P3 outputs are quantitative, however, they do not represent the absolute annual likelihood of experiencing a wildfire. Instead, the outputs provide a measure of relative likelihood, subject to landscape alterations. A process/method is, therefore, needed to convert the burn probability map to the annual unavailability of a transmission line for risk assessment in power systems. A method is proposed to align a map with wood pole transmission lines and their corresponding burn probabilities in the area. The annual unavailability of a transmission line is assessed and applied in the loss of load and/or loss of energy studies to quantify the wildfire-related risks. The output can be utilized for capital project assessment and prioritization.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".