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Record W2119368778 · doi:10.24908/pceea.v0i0.3123

Publishing undergraduate engineering designs, a case study

2010· article· en· W2119368778 on OpenAlexaffvenue
Medhat Moussa, William David Lubitz, Antony Savich

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2010
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPublishingTerm (time)Engineering managementEngineeringMathematics educationEngineering ethicsComputer scienceSoftware engineeringPsychologyPolitical science

Abstract

fetched live from OpenAlex

Manyengineering schools require senior undergraduate students to complete term-long design projects.These projects are normally communicated using a major design report that is submitted for instructor evaluation, with no expectation of any additional audience for the work.This paper presents a case study in publishing undergraduate engineering designs using a short conference style paper format.The short paper has been integrated into a projectbased third year design course at the School of Engineering, University of Guelph.The course is taken by all engineering students at the same time and the design projects vary from environmental, to bioprocess and software design projects.The procedure used to help students formulate their designs as short papers is outlined.The papers are published in course proceedings that are publicly available through the University of Guelph Library's Atrium web portal.To ensure uniform style and format, papers are prepared in LaTeX, which is also used to assemble the final proceedings.A LaTeX kit will be made available for other universities that wish to follow the same procedure.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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
Published2010
Admission routes2
Has abstractyes

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