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Long-Term Microhardness Evolution in Ti-Ni Shape Memory Alloys Processed by Severe Cold Rolling

2008· article· en· W1973512625 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 · 2008
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsÉcole de Technologie Supérieure
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsMaterials scienceIndentation hardnessSofteningAusteniteMetallurgyHardening (computing)Shape-memory alloyAlloyComposite materialMicrostructure

Abstract

fetched live from OpenAlex

Ti-50.26at.%Ni shape memory alloy samples were subjected to cold rolling (CR) with true strains encompassing from moderate (logarithmic strain e=0.25) to severe (e=2.1) deformation. СR with e = 0.5 and more initiated a partial austenite amorphization. The evaluation of structural changes in the material during its long-term storage was performed using Vickers microhardness (HV) technique. It was shown that during storage at room temperature up to 9 months, microhardness varied following a dome-shaped trend, thus reflecting commonly encountered interaction between two concurrent time-dependent phenomena, the first responsible for the material hardening, and the second, for the material softening. To represent such phenomena, a simple mathematical model was proposed and experimentally validated.

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)0.002
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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