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Record W2159020827 · doi:10.1109/te.2007.910362

Characterizing a Thermoelectric Module as Part of a Semiconductor Course Laboratory

2008· article· en· W2159020827 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

VenueIEEE Transactions on Education · 2008
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsThermoelectric effectSemiconductorSeebeck coefficientThermoelectric materialsThermal resistanceMaterials scienceThermoelectric generatorThermal conductivityThermalSemiconductor deviceEngineering physicsElectrical resistance and conductanceOptoelectronicsElectronic engineeringMechanical engineeringComputer scienceNanotechnologyEngineeringPhysicsThermodynamicsComposite material

Abstract

fetched live from OpenAlex

Thermoelectric modules (TEM), also known as Peltier modules, form an interesting topic to cover in an introductory semiconductor course. They provide insight on some important semiconductor principles, namely the Seebeck effect (the reverse of the Peltier effect) and thermal conductance. This paper presents a new methodology to characterize a TEM using a custom-designed test apparatus along with a simplified method for determining the TEM's three key parameters: its Seebeck coefficient, its electrical resistance, and its thermal conductance. Results obtained using this methodology were validated by comparing the anticipated theoretical behavior of the TEM (using the experimentally determined parameters) to the actual results obtained in a vacuum environment. The suggested methodology has been evaluated by students and the results of this demonstrate its usefulness in an educational environment.

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 score0.046
Threshold uncertainty score0.615

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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