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Record W1954240464 · doi:10.24908/pceea.v0i0.4032

TEACHING DESIGN IN AN UNDERGRADUATE HEAT TRANSFER COURSE

2011· article· en· W1954240464 on OpenAlexvenueaboutno aff
A. Dolatbadi, Rajat Dhiman, S. Chandra

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsHeat transferElectronicsHeat sinkMechanical engineeringPrinted circuit boardCourse (navigation)EngineeringComputer scienceElectrical engineeringAerospace engineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Third year undergraduate mechanical engineering students at the University of Toronto take a one-semester course in heat and mass transfer that is taught as a course in design of electronic cooling systems, combining theory with design and experiments. At the start of the course students are introduced to heat transfer problems faced by the electronics industry and cooling technologies. Heat transfer theory is then presented by analyzing electronic cooling systems. A combined numerical and experimental project is given to design a cooling system for an electronic instrument. Students are given a kit that includes a circuit board, heat sinks and a cooling fan. Components generating heat are represented by square aluminum plates clamped around thin heaters that can be placed anywhere on the circuit board. Students write a computer code to solve heat transfer equations and predict temperature distributions in the circuit board. The accuracy of these predictions is verified by experimental measurements. Results are submitted in the form of a report written from the perspective of a thermal design engineer working in a company that manufactures electronic equipment.

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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.212
Teacher spread0.200 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2011
Admission routes2
Has abstractyes

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