Perspectives on Industry-Sponsored Capstone Projects Within Mechanical and Materials Engineering at Western University
Bibliographic record
Abstract
Capstone projects that are conducted in final year study within Mechanical and Materials Engineering at Western University, traditionally, have been either student-originated, faculty-originated, or facilities-originated. Capstone projects provide students with an opportunity to apply what they have learned in their studies to an actual problem in a team setting. Recently, the capstone project course had experienced revisions with a desire to increase the number of industry and community-sponsored projects. Industry-sponsored capstone projects can provide value, not only to the sponsoring company, but also to the participants who are afforded the opportunity to work on a relevant industry problem. There are intrinsic benefits for students having worked on a project that can, depending on the outcome, be used by the industry sponsor to add value to their operations. Particularly, where student capstone projects are externally funded, there exists the expectation that the project outcomes will be in a usable form. In recognizing the value of industry-sponsored capstone projects, we have implemented a rigorous method of capstone project oversight based upon the experience of other academic institutions, existing literature, and employing conventional project management strategies and tools. This paper discusses some of the challenges associated with industry-driven capstone projects and some manners in which we have attempted to address them.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.012 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".