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Record W1843434340 · doi:10.24908/pceea.v0i0.4687

INTEGRATING REGIONAL INDUSTRIAL THEME EXAMPLES IN PROCESS CONTROL EDUCATION

2012· article· en· W1843434340 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsTheme (computing)Multidisciplinary approachProcess (computing)Context (archaeology)Control (management)Computer scienceSoftwareEngineering managementManufacturing engineeringEngineeringArtificial intelligenceSociology

Abstract

fetched live from OpenAlex

A process control course was elaborated around the specific regional (industrial) context in which UQAR has an important mission of regional development. A multidisciplinary approach is used, integrating notions from various fields of engineering (electrical, mechanical, chemical and civil engineering) through theme examples such as wastewater treatment, pulp and paper making, mining and metallurgical extraction (mineral grinding). Laboratory activities on such processes are realized using a simulation software specifically designed for process control education. The small size of the groups at UQAR also allows to employ innovative strategies on how to run the activities and to evaluate the students. One laboratory on a real physical system (electrical motor) was also part of the course, to balance between the advantages of the software and the more “hands-on” laboratories. General feedback and comparative appreciation from students is then presented, followed by overall conclusions

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.921

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.219
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