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Record W2313362036 · doi:10.7227/ijmee.32.2.2

Quick-Return Mechanism Design and Analysis Projects

2004· article· en· W2313362036 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.

Bibliographic record

VenueInternational Journal of Mechanical Engineering Education · 2004
Typearticle
Languageen
FieldEngineering
TopicMechatronics Education and Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMechanism (biology)Computer scienceCrankFour-bar linkageMotion (physics)Artificial intelligence

Abstract

fetched live from OpenAlex

Quick-return (QR) mechanisms feature different input durations for their working and return strokes. The time ratio ( TR) of a QR mechanism is the ratio of the change in input displacement during the working stroke to its change during the return stroke. Several basic types of mechanism have a QR action. These types include slider-crank and four-bar mechanisms. A project on QR mechanism design, within a first course on the theory of mechanisms, has been found to be effective for exposing students to concepts of mechanism design and analysis. This paper reviews basic QR mechanisms, presents a project problem and solution examples, and discusses the value of inclusion of such project problems within theory-of-mechanism courses.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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