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Record W2164279734 · doi:10.24908/pceea.v0i0.3624

A Mechanical Dissection Laboratory using KitchenAid Mixers

2011· article· en· W2164279734 on OpenAlexaffvenueabout
Ted Hubbard, Darrel A. Doman, Craig Arthur

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBevel gearDurabilityMechanical engineeringBevelEngineeringWork (physics)Engineering drawingTest equipmentComputer scienceManufacturing engineering

Abstract

fetched live from OpenAlex

This paper presents the development of a Mechanical Dissection Laboratory at Dalhousie University using KitchenAid stand mixers. In addition to a reputation for durability, the mixers are very well designed mechanically, and thus serve as an excellent teaching tool for Machine Design. The mixers contain a large number of robust and relevant components, covering almost all of the topics discussed in most Machine Design courses. In this lab students are introduced to topics such as electric motors, planetary gears, helical and spur gears, worm gears, bevel gears, shafts and couplers. Students work in small groups of approximately three; they are provided with a tool kit, as well as a disassembly manual. After a short safety introduction, the students disassemble the device, taking measurements as they proceed. They then re-assemble the device, and test that it is in complete working order.

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)0.001
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0410.014

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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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
GenreOther

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
Published2011
Admission routes3
Has abstractyes

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