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Record W4391956887 · doi:10.18260/1-2--36970

Development of Attachments for the Quanser Qube

2024· article· en· W4391956887 on OpenAlexaff
Diane Peters, Aaron-Joseph Jones

Bibliographic record

Venue2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsQuanser (Canada)
Fundersnot available
KeywordsComputer scienceMATLABSet (abstract data type)ServoSoftwareInverted pendulumTurbineServomotorLinkage (software)SimulationOperating systemProgramming languageMechanical engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The Quanser Qube is an integrated servomotor lab hardware platform [1], shown in Figure 1. This platform includes not only the direct-drive brushed DC motor, but also two encoders as well as the data acquisition system. One encoder is used to measure the rotation of the DC motor’s shaft itself. It is supplied with two standard items, an inertia disk and an inverted pendulum. The inertia disk is a small aluminum part, which mounts to the equipment using magnets. The inverted pendulum also mounts to the equipment with magnets, with an encoder that plugs into the equipment to provide an additional sensor input to the system. The system includes an amplifier and other necessary components in order to be controlled with either LabVIEW or with MATLAB/Simulink, with the LabVIEW control requiring the National Instruments myRIO device [1].

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.100
Threshold uncertainty score0.334

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0030.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1000.045

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 head0.066
GPT teacher head0.306
Teacher spread0.239 · 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 designBench or experimental
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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