Bibliographic record
Abstract
Electricity networks are most efficient and reliable when supply and demand are balanced.This equilibrium is typically achieved through the expansion of generation sources, the use of reserves and the enhancement of the capacity of transmission and distribution networks.However, continuing to rely on such techniques alone represents an unsustainable and challenging pathway, as the electrification of heating, cooling and transport is projected to surge in the future, thereby greatly increasing peak electricity demand.In many nations across the globe, peak electricity demand periods are infrequent yet constitute a significant portion of the total electricity generation cost, even though much of the installed capacity to meet them is under-utilized throughout the year.Peak demand often stresses the power network, reducing its reliability, and relies predominantly on peak power plants that exacerbate greenhouse gas emissions.In turn, electricity costs can increase significantly following the introduction of fossil fuel taxation, leading to an increase in the percentage of people living beneath the energy poverty line (defined as households spending more than 10% of their income on electricity).Reports indicate that energy poverty disproportionately affects lower-income individuals (particularly single-parents with a dependent child and elderly) and at present a significant portion of such households can be characterized as "energy-poor".
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".