Lessons learned from the York University Rover Team (YURT) at the university rover competition 2008
Bibliographic record
Abstract
In this paper, we explore the lessons learned from the work of the York University Rover Team, which designed, built, and operated prototype rovers for the University Rover Challenge 2008 and 2009, placing third in the first year, and winning first place in the second year. We outline the competition and the team with a brief description of the York University space engineering program. The design of the rover is described with emphasis on the technical challenges of engineering a reliable system. Also, the value of this project as an educational medium is evaluated with respect to traditional classroom learning. The University Rover Challenge 2008 took place in June 2008, at the Mars Research Desert Station (MDRS) near Hanksville, Utah. Under a simulated Martian environment, competing teams remotely performed four mission critical tasks using one remotely-operated robotic system (a rover) of maximum 50kg mass. The competition was continued in June 2009, with some changes to the tasks and requirements. This is one of several engineering projects aimed at providing experiential education to engage science and engineering students through hands-on experience. With participating students from wide range of disciplines, the project proved to be an inter-disciplinary, cooperative educational tool.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".