MétaCan
Menu
Back to cohort
Record W2038635261 · doi:10.1117/12.538234

The influence of composition and thermomechanical treatments on the magnetic shape memory effect of Ni-Mn-Ga single crystals

2004· article· en· W2038635261 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

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsCrystal twinningMaterials scienceDifferential scanning calorimetryShape-memory alloyMartensiteDiffusionless transformationMagnetic shape-memory alloyWork (physics)Stress (linguistics)Thermomechanical analysisMetallurgyComposite materialThermodynamicsMagnetic fieldMagnetic domainMicrostructureMagnetizationThermal expansion

Abstract

fetched live from OpenAlex

In the current work, repeated mechanical and magnetic forces have been applied to Ni-Mn-Ga samples with different compositions and different thermomechanical histories in order to determine the combined effects of these parameters on the magnetic shape memory effects, especially the magneto-mechanical properties, of these alloys. The results demonstrate that prior history has strong influence on the twinning start stress and twinning strain. In addition, heat treatment of the materials seems to increase the amount of strain that can be obtained (up to the theoretical limit). Moreover, there is indication that prior heat treatment may also affect the martensite crystal structure that is formed during cooling. In addition, the dependence of martensitic transformation on composition and prior thermomechanical treatments was also studied by differential scanning calorimetry (DSC) analysis.

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)0.001
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 score0.096
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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