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

A Mechanical Dissection Laboratory using KitchenAid Mixers

2011· article· en· W2164279734 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
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