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Yield strength of twin-belt cast Al–Mg–Sc–Zr alloy after annealing

2014· article· en· W2027953455 on OpenAlex
M. Heydarzadeh Sohi, Warren J. Poole, Chad W. Sinclair, M. Gallerneault

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 and Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsNovelis (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAlloyAnnealing (glass)Ultimate tensile strengthMetallurgyPrecipitation hardeningMicrostructureAluminiumPrecipitationWork hardeningDuctility (Earth science)Yield (engineering)Composite materialCreep

Abstract

fetched live from OpenAlex

AA5xxx aluminium alloys are used in the automotive and packaging industries owing to their high strength and ductility. The addition of Sc and Zr to these alloys has shown promise for improving high temperature stability and therefore broadening the range of applications. This high temperature stability is due to the formation of fine Al 3 (Sc, Zr) precipitates during aging. In this work, two twin-belt cast Al–3Mg alloys, one with 0·4 Sc and the other without Sc, were annealed at 300 and 400°C. Hardness, tensile yield stress and electrical resistivity measurements were used to examine the evolution of microstructure and strength of the alloys. These results were then utilised to develop a yield stress–precipitation model to describe simultaneous precipitation hardening and recovery.

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 score0.003
Threshold uncertainty score0.494

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 studies0.0000.001
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 head0.006
GPT teacher head0.189
Teacher spread0.184 · 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