MétaCan
Menu
Back to cohort

Dislocation slip distance during compression of Al–Si–Cu–Mg alloy with additions of Ti–Zr–V

2014· article· en· W2004538023 on OpenAlexaff
S.K. Shaha, Frank Czerwiński, D.L. Chen, W. Kasprzak

Bibliographic record

VenueMaterials Science and Technology · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsNatural Resources CanadaToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceAlloySlip (aerodynamics)MicrostructureDislocationHardening (computing)MetallurgyDeformation (meteorology)Compression (physics)Atmospheric temperature rangeComposite materialThermodynamics

Abstract

fetched live from OpenAlex

The plastic deformation behaviour of the Al–Si–Cu–Mg alloy with micro-additions of Zr–V–Ti was measured in the temperature range of 298–673 K and the true stress–true strain compression curves were used to calculate the dislocation slip distance (DSD). A new constitutive equation for the temperature dependent DSD was developed, based on Mott's theory of strain hardening. The DSD predicted by the model was in good agreement not only with values achieved for the Al–Si–Cu–Mg alloy tested but also for other Al based and Pb–Sb alloys with deformation data available in the literature. A comparison of deformation and microstructure suggests that the grain refinement during hot compression deformation occurring due to continuous dynamic recrystallisation is responsible for a drastic growth of the DSD.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations27
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueMaterials Science and TechnologySame topicMicrostructure and mechanical propertiesFrench-language works237,207