Harnessing electric energy from vehicle induced wind gust
Bibliographic record
Abstract
This paper is focused on simple demonstration of yet untapped possibility to generate electricity from vehicle induced wind gust (VIWG). As such enormous energy resource continues to waste daily along millions of world's vehicle paths, this paper through simple demonstrative design, seeks to redirect researchers and engineers towards a new thinking in the area of VIWG resource. Here, a self-sustaining apparatus designed around the features of simple ratchet mechanism demonstrates the ability to harness energy from the kinetic energy inherent in VIWG. For ease of reference this apparatus is referred to as Alternative Power Generating Machine (APGM). Data culled from a research work of the Ministry of Transportation, Ontario, Canada served as the source of basic data for the detailed design of the APGM. The simplicity of the APGM holds its key to the market. APGM could sustain facilities such as modern LED-based street lighting technologies, traffic lights or be utilized for electric car/battery recharging units. The machine is very easy to install and would operate effectively at safe distance from the curb of selected vehicle path. The current model of the APGM presented in this paper is a first attempt, hence has lots of room for future improvement. Recommendations are suggested to advance the current model in order to multiply the current power output. This work without doubt, would open new window into many possibilities in alternative and greener energy source.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".