TRENDS AND CHALLENGES OF ENERGY EFFICIENCY DEVELOPMENT IN SLOVENIAN INDUSTRY
Bibliographic record
Abstract
1. Energy Efficiency Centre, Jozef Stefan Institute, Slovenia; email: matevz.pusnik@ijs.si 2. Energy Efficiency Centre, Jozef Stefan Institute, Slovenia; email: fouad.al-mansour@ijs.si 3. Energy Efficiency Centre, Jozef Stefan Institute, Slovenia; email: boris.sucic@ijs.si 4. Energy Efficiency Centre, Jozef Stefan Institute, Slovenia; email: matjaz.cesen@ijs.si Abstract Energy efficiency measures and utilization of renewable energy sources have been consistently incorporated into energy strategic documents of the member states, addressing various sectors. Industry, being the backbone of the European economy, is still not sufficiently addressed, since its development is almost exclusively market driven. The importance of industrial sector for the economy is not questionable, nor its impact on the environment. More than a quarter of all final energy consumption in Europe can be attributed to industrial sector, representing one third of final energy consumption of natural gas and one third of electricity use, with more than three quarters of all final energy consumption of solid fuels. The paper presents an overview of the energy efficiency development trends in Slovenian industry. To assess the energy efficiency development, the energy efficiency index (ODEX), has been applied to the Slovenian industrial sector, also highlighting some of the non-technical changes. The methodological part of this study is significantly complemented with the data, obtained from the extensive cooperation with the real industrial environment, bridging the gap between statistics, policies and practice.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".