Benefit Of Student Participation In Advanced Vehicle Technology Competitions
Bibliographic record
Abstract
For the past 21 years the U.S. Department of Energy has sponsored more than 45 Advanced Vehicle Technology Competitions (AVTCs) with management provided by Argonne National Laboratory.Through partnerships between government, industry, and academia, engineering students have had the opportunity to explore sustainable vehicle solutions while at the same time enriching their educational experience.In this paper the benefit to students is described based on experience gained in the recently completed Challenge X competition and the ongoing EcoCar challenge.In both competitions students from 17 universities throughout North America have had the opportunity to re-engineer a production vehicle to improve the fuel economy, reduce emissions, and increase performance.Additional support has been provided by General Motors, the Natural Resources Canada, and numerous automotive parts suppliers.The competitions are intense, profound endeavors for the students that result in technological developments, proficiency with today's engineering tools and methodology, and a unique team experience.They serve as a means for students to acquire practical engineering experience in an environment that requires cooperation and team work from students in several engineering fields.In addition to the technical concepts, students are required to consider the aesthetics of design, consumer acceptability, and how the vehicle would be marketed.Students from disciplines outside engineering, such as in business and communications, add another dimension to the competition.While the coordination of these diversified groups of students can be a challenge for the faculty advisor, the students develop an understanding and appreciation for what each discipline can contribute.Students quickly learn that the breadth and depth of the project requires a multi-faceted approach where team work is essential.In a relatively short period of time, students must acquire specialized automotive knowledge and proficiency with numerous software tools.In recent AVTC competitions students are required to emulate the design and development process employed by automotive manufacturers.Students compare vehicle architectures and various advanced technologies so as to select components that enable them to satisfy vehicle technical specifications (VTS) that they have fine tuned from the original vehicle criteria established by the competition organizers.Throughout the competition, the VTS serves as a baseline for comparing design objectives with actual vehicle performance.Students are provided with advice and mentoring throughout the process by engineers engaged in the automotive industry.The actual construction of the vehicle and eventual competition provides a unique experience that enriches their educational experience and provides employment opportunities.Page 15.226.2
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.023 |
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".