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Record W3111940596 · doi:10.3934/matersci.2020.6.836

Shape memory alloy heat activation: State of the art review

2020· article· en· W3111940596 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.

Bibliographic record

VenueAIMS Materials Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsWestern University
Fundersnot available
KeywordsShape-memory alloySMA*PseudoelasticityMaterials scienceHeat transferMechanical engineeringAerospaceBiocompatibilityHeat resistanceComputer scienceEngineeringComposite materialMetallurgyMartensiteMechanicsAerospace engineering

Abstract

fetched live from OpenAlex

The use of shape memory alloys (SMAs) in civil structures attracted the attention of researchers worldwide. This interest is due the unique properties of SMAs, the shape memory effect (SME) and pseudoelasticity. Other desirable attributes for SMA include biocompatibility, high specific strength, wear resistance, anti-fatigue characteristics, and high yield strength. These beneficial qualities allow for a wide range of applications ranging from aerospace field to the medical and civil engineering fields. To intelligently utilize shape memory alloys and widen their application potential, certain characteristics including the heat activation and heat transfer mechanisms must be thoroughly understood. Such understanding would allow the development and optimization of systems utilizing SMA. Hence, this paper presents a state-of-the-art review about shape memory alloys with specific focus on the heat activation mechanisms and the heat transfer modes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.006
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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.263
Teacher spread0.234 · 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