Harvesting Energy from Roadways for Smart and Sustainable Cities
Bibliographic record
Abstract
Industry, modernization, and urbanization started in the 21st century.At present, we live lavishly with everything automated and machine-driven.Cars and expensive vehicles use more gas.A.C. and other features are energy intensive.Road cars waste energy by vibration, heat, etc.There are various ways to harness waste and renewable energy.In this work, harvesting energy through solar energy, geothermal energy, piezoelectric technology, thermoelectric generators and wind energy installation on the roadside and their cost comparison analysis have been analysed.Furthermore, the air density, wind velocity and power output from wind turbines are analyzed in major cities of India (Like Chennai, Erode, Coimbatore, Bangalore, Mumbai and Delhi).For analysis, a 200 W wind turbine is considered, and it develops a maximum power output of 3.47 W in Bangalore and Coimbatore cities and a low power output of 3 W in Mumbai when the cross-sectional area of the turbine is 1 m 2 .The results indicate huge potential for developing harvesting energy on the roadside through different energy sources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".