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Electrical Resistivity Measurements: A Sensitive Tool for Studying Aluminium Alloys

2006· article· en· W2051362204 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMaterials science forum · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCopper Interconnects and Reliability
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrical resistivity and conductivityMaterials scienceAlloyMicrostructureAluminiumDislocationMetallurgyCharacterization (materials science)Electrical conductorComposite materialNanotechnologyElectrical engineering

Abstract

fetched live from OpenAlex

This paper examines the challenges which are encountered when using electrical resistivity measurements for characterization of microstructures in aluminum alloys. Experimental examples are provided of electrical resistivity studies conducted on two aluminum alloys, a heattreatable alloy (AA6111) and a non-heat-treatable alloy (AA5754), which demonstrate how the technique can be used to characterize changes in the microstructure. Results on AA6111 show that the dependence of the measurement on solute atoms and fine scale precipitates makes deconvolution of the resistivity signal non-trivial and therefore, utilization of supplementary technique(s) in conjunction with electrical resistivity measurements is essential. In the next example, room temperature electrical resistivity measurements as a function of cold work for AA5754 illustrate a larger resistivity contribution from dislocations in this alloy as compared to that reported for pure aluminum. The interaction of solutes and dislocations is cited as the possible source for the increased dislocation contribution.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.029
GPT teacher head0.278
Teacher spread0.249 · 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