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Record W2470241942 · doi:10.1177/0967391120000807449

Damage Evaluation by Means of Electrical Resistivity Measurements

2000· article· en· W2470241942 on OpenAlexaff
Yumei Cao

Bibliographic record

VenuePolymers and Polymer Composites · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsQueen's University
Fundersnot available
KeywordsElectrical resistivity and conductivityMaterials scienceComposite materialComposite numberReproducibilityPerpendicularDrop (telecommunication)Matrix (chemical analysis)

Abstract

fetched live from OpenAlex

Electrical resistivity has been used to detect the internal damage inglass fibre-polyester composite sheet materials. It is shown that theintroduction of microstructural damage to the composite increases theelectrical resistivity in a direction perpendicular to the plane in which thefibres lie. In these experiments, the samples were subjected to somepredetermined value of impact energy using a drop weight tester. The impactenergy causes fibre-matrix debonding and microcrack propagation within thematrix and fibres. The measurement of the electrical resistivity before andafter impact reveals a linear relation between the electrical resistivity andthe magnitude of the applied impact energy. The reproducibility of the data washigher (within ±2%) for the less damaged samples. For the more severelysamples, however, the reproducibility was poorer (within ±8%). This is a goodfeature, as the extent of damage in the less severely damaged samples cannot bedetected by visual inspection.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.232
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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
Published2000
Admission routes1
Has abstractyes

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