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Record W2314611543 · doi:10.1021/am505140f

Magnetic Field Switchable Dry Adhesives

2015· article· en· W2314611543 on OpenAlexafffund
Jeffrey Krahn, Enrico Bovero, Carlo Menon

Bibliographic record

VenueACS Applied Materials & Interfaces · 2015
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdhesionMaterials scienceAdhesiveMagnetic fieldPaint adhesion testingComposite materialStiffnessPreloadNuclear magnetic resonance

Abstract

fetched live from OpenAlex

A magnetic field controllable dry adhesive device is manufactured. The normal adhesion force can be increased or decreased depending on the presence of an applied magnetic field. If the magnetic field is present during the entire normal adhesion test cycle which includes both applying a preloading force and measuring the pulloff pressure, a decrease in adhesion is observed when compared to when there is no applied magnetic field. Similarly, if the magnetic field is present only during the preload portion of the normal adhesion test cycle, a decrease in adhesion is observed because of an increased stiffness of the magnetically controlled dry adhesive device. When the applied magnetic field is present during only the pulloff portion of the normal adhesion test cycle, either an increase or a decrease in normal adhesion is observed depending on the direction of the applied magnetic field.

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)0.000
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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

Citations48
Published2015
Admission routes2
Has abstractyes

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Same venueACS Applied Materials & InterfacesSame topicAdhesion, Friction, and Surface InteractionsFrench-language works237,207