FROM MECHANICAL ENGINEERING CAPSTONE DESIGN TO DESIGN IMPLEMENTATION
Bibliographic record
Abstract
Traditionally, mechanical engineering capstone courses focused on teaching students the application of fundamental engineering theory to complex mechanical designs. Recently, there has been a transition towards experiential learning initiatives, such as prototyping, in engineering design. This paper looks at the relationship between the mechanical engineering design capstone course and a course in product design and development, which provides students with the opportunity to build prototypes of their designs, at the University of Ottawa. The importance of the traditional capstone course is considered and the implications of implementing these designs are examined. Many capstone design projects would require extensive work so that they could be implemented. A large hurdle appears to exist between analytical design and design implementation, and the term time constraints limit the complexity of designs intended for prototyping. In fact, students require many design iterations before they can build full-scale functional prototypes of their design. Therefore, we have observed that simple products work best for teaching design implementation.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".