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Record W1814154117 · doi:10.24908/pceea.v0i0.4792

DESIGNING AN UNDERGRADUATE FOOD PROCESSING LABORATORY FOR THE UNIVERSITY OF WATERLOO

2013· article· en· W1814154117 on OpenAlexafffundvenue
Yung Priscilla Lai, Kyung Eun Kate Sun, Christine Moresoli, Marc G. Aucoin

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2013
Typearticle
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooUniversity of Toronto
KeywordsFood processingFood industryProcurementEngineering managementFood engineeringFood plantEngineeringComputer scienceManufacturing engineeringMarketingBusinessFood science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.177
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes3
Has abstractyes

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