Energy and exergy assessment with updated Reistad estimates: A case study in the transportation sector of Bangladesh
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Transportation sector is one of the core parts of modern civilization. Proper utilization of energy and exergy in this sector is necessary to ensure energy loss and environmental sustainability. Increasing exergy efficiency will reduce carbon emissions from this sector. Since 1970, Reistad estimates have been widely used to determine the energy and exergy efficiencies of this sector. However, the modern transport sector has undergone significant changes in recent decades. Hence, it is necessary to apply new Reistad estimates in determining the energy and exergy efficiencies. This is the first study to apply updated Reistad estimates to explore the energy and exergy efficiencies in the transportation sector of Bangladesh based on the data from 2000 to 2017. The overall exergy efficiency is significantly lower than the energy efficiencies as it ranges from 27.7% to 30.0%. Efficiencies are lower as the maximum portion of input exergy is lost to the environment. The road subsector needs major improvements as it is responsible for major amount of exergy loss. A comparison is made between conventional and updated estimates which highlights that the updated estimates provide more accurate results. Thus, it is recommended to apply updated Reistad estimates in determining the energy and exergy efficiencies of the transport sector.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it