SERENE, LocalRES, SUSTENANCE:A multi-disciplinary approach to increase the local adaption of energy-efficient solutions
Bibliographic record
Abstract
The growing demand for accessible, locally available energy solutions, combined with the urgency to implement new regulations for the clean energy transition, highlights the need for widespread adoption of renewable energy on a much larger scale than is currently in place. Households account for 36% of total energy consumption in the EU (source: European Parliament), making them one of the largest contributors to overall energy usage. Their adoption of energy-efficient solutions is critical for achieving Europe’s climate goals. The SERENE project group, consisting of three EU-funded projects (SERENE, LocalRES, SUSTENANCE), is developing cutting-edge solutions to transform energy management and empower citizens and municipalities. Together, these projects aim to foster innovation, promote energy efficiency and renewable energy, and drive the transition towards a decarbonised energy system. Supported by the Horizon Results Booster (HRB) programme of the European Commission, the SERENE group collaborates to disseminate their findings and address the key challenges associated with scaling renewable energy adoption across Europe.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.017 |
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".