DESIGNING AN UNDERGRADUATE FOOD PROCESSING LABORATORY FOR THE UNIVERSITY OF WATERLOO
Bibliographic record
Abstract
Food processing is one of the largest industries in the world, making it an attractive field for chemical engineering students to pursue. Currently, there is no food processing laboratory at the University of Waterloo. Consequently, students do not have an opportunity to link fundamental chemical and biological engineering concepts germane to the food processing industry to tangible applications. The solution is to design a versatile undergraduate food processing laboratory to enhance the engineering undergraduate experience. The laboratory would have three unit operations ubiquitous to the food industry. The three selected unit operations were spray drying, micro-encapsulation, and extrusion. Suppliers and/or providers of processing equipment, raw ingredients, and pest control services were identified with the consideration of health and safety recommendations. The food processing laboratory layout was created with consideration of minimizing workplace hazards and the risk of food contamination. The cost of running the laboratory for the first year along with equipment/materials procurement was estimated to be around $1 million CAD in an existing room at the university. By providing a food processing laboratory, chemical engineering students would be supplied a contained learning environment along with the incentive to consume their manufactured products.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".