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Record W4251706474 · doi:10.1016/s1365-6937(21)00160-x

H2O Innovation, Canada

2021· article· en· W4251706474 on OpenAlexaboutno aff

Bibliographic record

VenueFiltration Industry Analyst · 2021
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
Fundersnot available
KeywordsCreepMaterials scienceAlloyAnnealing (glass)MicrostructureResidual stressAgglomerateMetallurgyComposite material

Abstract

fetched live from OpenAlex

This study aims to enhance the dimensional stability of alumina dispersion strength copper alloy via creep annealing treatment. The microstructure, residual stress and micro-yield strength of alloys after different treatments were investigated. During creep annealing, the residual stress in the alloy was dramatically reduced due to the decrease of dislocation density. The nano-Al2O3 particles in the alloy did not coarsen or agglomerate during the high temperature treatment and retarded the growth of subgrains effectively, which played important roles in high micro-yield strength of creep annealed alloy. The pores were effectively reduced by the creep deformation during creep annealing, resulting in a more uniform residual stress distribution and an increase in micro-yield strength. Compared with extruded alloy, the residual stress of alloy creep annealed at 950 °C for 12 h was reduced by 88%, while the micro-yield strength increased by 9.7%, leading to a significant enhancement in dimensional stability of the alloy.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.193
Teacher spread0.180 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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