Bertoldi: “Major Households Appliances in New Member States and Candidate Countries: looking for energy savings
Bibliographic record
Abstract
The Commission, through its Joint Research Centre (JRC) has focused a number of its activities on electricity end uses including domestic appliances. Better equipment efficiency is the fastest and most cost-effective response to limiting growing demand for electricity and to reduce CO2 emissions. Every unit of energy saved on electricity corresponds to about 3 units of primary energy avoided. And all of these are part of every day life and level of comfort. [1] The status of the electricity end-use consumption is well known for the EU-15, but there is still a lack of information about New Member States (NMS) and Candidate Countries (CC). NMS-10 together with Romania and Bulgaria that is possible to join EU in 2007 represents around a quarter of the population and existing households from EU-15.The household electricity consumption is mainly due to appliances and lighting and in these countries the saving potential is much greater than in the EU-15. The aim of the paper is to show the status of electricity consumption for main appliances and to try to identify the saving potential for these in New Member States and Candidate Countries.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".