Bibliographic record
Abstract
Abstract Designing efficient energy systems is a significant challenge. In a world with finite natural resources and large energy demands, it is important to understand not only actual efficiencies but also limits to efficiency, as the latter identify margins for efficiency improvement. Energy analysis methods, which yield energy efficiencies, do not provide limits to efficiency. To obtain meaningful and useful efficiencies for energy systems, and to clarify losses, exergy analysis is a beneficial and useful tool. Exergy efficiencies establish upper limits to efficiency and provide a measure of approach to ideality. This is the focus of this article. Limits to efficiency are subject to two constraints, which are often not clearly understood: theoretical and practical. The energy utilization of systems as small as a device to as large as a country can be assessed using exergy analysis to gain insights into efficiency; examples of the benefits of applying exergy to such examples are given. Furthermore, the insights gained through the exergy analyses presented here in terms of meaningful efficiencies and quantified margins for improvement are examined to determine their range of applicability. Exergy analyses are shown to be able to provide useful information about devices and regions such as a country, including limiting and actual efficiencies, and can consequently help achieve savings in resource use through efficiency, conservation, and other measures. Exergy analyses also help identify margins for improvement, and thus are useful for informing energy planning and research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".