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Record W2518564975 · doi:10.18260/1-2--7744

An Innovative Mechatronics Course For A Traditional Mechanical Engineering Curriculum

2024· article· en· W2518564975 on OpenAlexaffabout
Peter Wild, Brian Surgenor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechatronics Education and Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsMechatronicsCurriculumEngineeringEngineering educationComputer scienceElectrical engineeringEngineering management

Abstract

fetched live from OpenAlex

Many Mechanical Engineering Departments have recently or are now developing programs and/or courses in mechatronics.The emphasis of these programs/courses varies from institution to institution.The programs at four Canadian universities are described briefly and a new elective course in mechatronics at Queen's University is described in detail.The primary objective of the course is to create a sense of opportunity and excitement about mechatronics system design.This course focuses on the practical implementation of simple mechatronic systems with particular emphasis on the electronics for conditioning and interfacing of sensor signals and driver control signals.The laboratory portion of the course is based on the Basic Stamp II, a user friendly microcontroller from Parallax Inc.By the end of the course, students understand a relatively simple system of sensors and actuators under the supervision of a microprocessor engaged in on/off or simple PID control.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.193
Threshold uncertainty score0.647

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0020.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1930.097

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 head0.025
GPT teacher head0.276
Teacher spread0.251 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2024
Admission routes2
Has abstractyes

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