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Record W2910902518 · doi:10.24908/pceea.v0i0.12989

A new laboratory for the students of the Faculty of Engineering at the University of Sherbrooke to support the characterization and the validation of their prototypes

2018· article· en· W2910902518 on OpenAlexafffundvenueabout
Jonathan Nadeau, Alain Desrochers, João Pedro F. Trovão

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversité de Sherbrooke
FundersCentre québécois de recherche et de développement de l’aluminiumNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCapstoneProduct (mathematics)Engineering managementProcess (computing)Graduate studentsEngineeringCapstone courseProduct designField (mathematics)Test (biology)Software engineeringComputer scienceSystems engineeringMedical educationMedicine

Abstract

fetched live from OpenAlex

In order to help the students of the Faculty of Engineering at the Université de Sherbrooke within the field of product design in engineering, the Laboratory for the Characterization and the Validation of Prototypes (LCVP) has been created. The facilities feature most of the resources needed to conduct experiments toward the estimation and measurement of critical parameters and specifications along the design process but also toward the final validation of a product design. To that end the support of a professional researcher is provided to advise the students toward the proper implementation of their test benches. Overall, the LCVP provides state-of-the-art equipment and competent resources to the students from various departments at both undergraduate and graduate levels therefore improving their product design experience, while enhancing their competencies. This is indeed a unique feature of the LCVP, setting it apart from other initiatives targeted at supporting capstone and student projects.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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 designObservational
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

Citations1
Published2018
Admission routes4
Has abstractyes

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