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Record W1519755541 · doi:10.1109/ceidp.2003.1254783

Characterization of dielectric materials using a high-resolution scanning Kelvin-microprobe

2004· article· en· W1519755541 on OpenAlexaff
Larissa-Emilia Cheran, Peter G. Lam, Zhong Zheng, S.A. Boggs, Michael Thompson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsMicroprobeDielectricCharacterization (materials science)Kelvin probe force microscopeMaterials scienceResolution (logic)MicrostructureImage resolutionOptoelectronicsAnalytical Chemistry (journal)NanotechnologyOpticsMineralogyComposite materialAtomic force microscopyChemistryComputer sciencePhysics

Abstract

fetched live from OpenAlex

The scanning Kelvin microprobe is a powerful technique to characterize subtle changes in surface microstructure and local chemical properties through the simultaneously imaging of the topography and potential distribution across a surface at the sub-micron level. The study of dielectric materials using the Kelvin method opens a new area of applications for a technique traditionally reserved specifically for metals and, more recently, for semiconductor materials. We present here the capabilities of this new instrument, the characterization of dielectric samples and the related challenges, as well as Finite Element Analysis models of this particular type of insulating surfaces.

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 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 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 score0.288
Threshold uncertainty score0.329

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)0.0000.000

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 head0.251
Teacher spread0.242 · 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 teacher head, 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

Citations5
Published2004
Admission routes1
Has abstractyes

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