Industrial Sponsored Design Projects Addressed by Student Design Teams*
Bibliographic record
Abstract
Abstract We have developed and implemented a four‐quarter design sequence starting in the spring of the junior year. The first course focuses on having teams of students take an industrial based project from inception through a conceptual design process culminating in a final design specification. The senior year sequence is structured to have three‐five member teams function as a type of “engineering consultant firm” to address externally sponsored projects. The teams initially work with the sponsor to develop a “Product Design Specification (PDS)” as the foundation of the project. The teams then develop the conceptual design of the project during the fall quarter in order to get sponsor approval to move toward final implementation or prototype development during the winter and early spring teams. The course culminates with a day long symposium where each team makes formal presentations of their project and designs to the campus community, the sponsor representatives, and invited guests from the local community and potential industrial sponsors. The paper will present the specifics of the Junior and Senior level courses, brief overviews of the related Sophomore and Junior prerequisite courses, the method of obtaining the industrial sponsors, team formation process, sample projects, and assessment results from the first two offerings of the sequence.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".