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Record W6764792 · doi:10.2172/1049790

International Round-Robin Testing of Bulk Thermoelectrics

2011· report· en· W6764792 on OpenAlexaff
Hsin Wang, Wallace D. Porter, Jan König, Lidong Chen, Shengqiang Bai, Terry M. Tritt, Alex Mayolett, Charlene M. Smith, Fred R. Harris, Jeff Sharp, J. S. H. Lo, Holger Keinke, László I. Kiss

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsUniversité du Québec à ChicoutimiUniversity of WaterlooNatural Resources Canada
Fundersnot available
KeywordsRound robin testReliability (semiconductor)Thermoelectric effectThermoelectric materialsThermal diffusivitySeebeck coefficientElectrical resistivity and conductivityMaterials scienceComputer scienceReliability engineeringPhysicsElectrical engineeringMathematicsStatisticsThermodynamicsEngineeringPower (physics)

Abstract

fetched live from OpenAlex

Two international round-robin studies were conducted on transport properties measurements of bulk thermoelectric materials. The study discovered current measurement problems. In order to get ZT of a material four separate transport measurements must be taken. The round-robin study showed that among the four properties Seebeck coefficient is the one can be measured consistently. Electrical resistivity has +4-9% scatter. Thermal diffusivity has similar +5-10% scatter. The reliability of the above three properties can be improved by standardizing test procedures and enforcing system calibrations. The worst problem was found in specific heat measurements using DSC. The probability of making measurement error is great due to the fact three separate runs must be taken to determine Cp and the baseline shift is always an issue for commercial DSC. It is suggest the Dulong Petit limit be always used as a guide line for Cp. Procedures have been developed to eliminate operator and system errors. The IEA-AMT annex is developing standard procedures for transport properties testing.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.072
GPT teacher head0.303
Teacher spread0.231 · 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.

Study designBench or experimental
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

Citations2
Published2011
Admission routes1
Has abstractyes

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