Combining Electricity and Ecological Resilience - Towards a New Holistic Framework
Bibliographic record
Abstract
The complexity of the electricity system is increasing due to various transitions and events taking place within and outside of the electricity market such as increased loads from distributed power supplies.The risk for various disturbances may increase with these transitions and events, including non-electricity system related disturbances like climate change.There is an urgent need to improve resilience of the electricity system so that it can handle also low probability and high impact disturbances.The objective of this paper is to analyse seven resilience principles, originally developed for socio-ecological systems, and interpret them for the electricity system.Results from the analysis indicate that the resilience principles can be seen to represent different categories in the socio-technical system that is the electricity system.These categories are technology, learning, information, stakeholder, organisation, and governance.The resilience principles enable a holistic view of the electricity system, and they can function as a support during the work to increase resilience of the electricity system.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".