Biosystems Engineering Design Trilogy: An Overview
Bibliographic record
Abstract
In the fall of 1998, the Department of Biosystems Engineering at the University of Manitoba introduced a package of three courses to enhance the teaching of engineering design.The objective was to teach undergraduate engineers how to design by exposing them to the type of design environment they will encounter in industry.Fundamental to this environment is a real design problem provided by an industry collaborator.Consequently, our undergraduate students are now required to complete three, four-credit courses in consecutive years.In each course, the students work on an industry-based design problem in a team environment.For second-year students, the solution is conceptual.For third-year students, a detailed design must be produced.For fourth-year students, the detailed design must also include an economic analysis and an indepth engineering analysis.Interaction between classes is achieved by requiring each student to contract his or her services to a design team from another year.Engineering communication skills (i.e., oral, written, and drawings) and practical fabrication skills are emphasized throughout all three courses.A high level of coordination between the three courses has been achieved, culminating in a joint presentation of the design projects at a formal technical meeting of the Canadian Society of Agricultural Engineering.This paper will discuss the details of this "Design Trilogy" including some of the modifications that have taken place over the past four years.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.025 |
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".