Energy Consumption Pattern of Indian Transportation Sector and its Thermodynamic Analysis
Bibliographic record
Abstract
The energy consumption pattern and thermodynamic analysis has been carried out of the Indian transportation sector considering its four subsectors viz. roadways, aviation, railways, and shipping. A period of 14 years between 2001/02 and 2014/15 has been considered to find the thermal efficiency and irreversible losses. The energy and exergy flow diagrams and the improvement potentials for this important sector are also presented. It was found that during the study period the overall exergy efficiencies (21.04% to 21.58%) were slightly less than the corresponding energy efficiencies (22.23% to 22.73%). The performance of the transportation sector of India has been compared to that of selected countries and is found to perform better than that of most countries (Iran, Canada, United Kingdom, etc.). This work also highlights the sectoral energy utilization and locates the sources responsible for lower efficiency of the Indian transportation sector.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 teacher head, 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".