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Record W2799983299 · doi:10.1139/tcsme-2013-0073

MECHANICAL BEHAVIOR OF COPPER THIN FILMS SUBJECTED TO VARIOUS STRAIN RATE LOADINGS

2013· article· en· W2799983299 on OpenAlex
Kuan-Jung Chung, Chi-Feng Lin, Wei-Cheng Chiang

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2013
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialUltimate tensile strengthStrain rateCopperStrain (injury)BrittlenessSlow strain rate testingStress (linguistics)Tensile testingPolyimideModulusYoung's modulusYield (engineering)Elastic modulusMetallurgy

Abstract

fetched live from OpenAlex

In this study, a micro-force tensile testing machine (MTS Tytron 250) was applied to test the polyimide samples coated with different thicknesses of copper (500–1500 nm). The experiments using different strain rates (1.6 × 10 −4 to 1.6 × 10 −2 s −1 ) were conducted to the test vehicles. The results showed that the stress and strain of Cu films were strongly correlated with the strain rate and film thickness. The mechanical strength, yield stress, Young’s modulus, and maximum tensile stress, increase as the strain rate increases or the thickness decreases. Strain rate sensitivity rapidly increases as the thickness decreases from 750 to 500 nm to imply that the workhardening rate increases while the thickness decreases, resulting in a higher probability of brittle failure.

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 categoriesInsufficient 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 score0.021
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.013
GPT teacher head0.225
Teacher spread0.213 · 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