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Record W4361864136 · doi:10.58286/27801

Thin-Film Coupling Membrane

2023· article· en· W4361864136 on OpenAlexaff
Edward Ginzel, Rick MacNeil, Mauricio Molina

Bibliographic record

Venuee-Journal of Nondestructive Testing · 2023
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsResearch Institute for Aging
Fundersnot available
KeywordsWedge (geometry)Ultrasonic sensorCoupling (piping)Materials scienceComposite materialThin filmMembraneAcousticsOpticsChemistryNanotechnologyPhysics

Abstract

fetched live from OpenAlex

When using ultrasonic probes with removable wedges, the acoustic coupling between wedge and probe is usually effected by means of a gel or grease. This tends to evaporate over time and requires occasional replacement of the gel. In manual ultrasonic testing, where the kit is disassembled daily or when the wedges are often replaced with different angled wedges, this is not usually a problem. However, in a production environment where the same probe and wedge may be used for several days at a time, the requirement to re-couple the probe to the wedge might be inconvenient. This paper provides a description of a solid thin-film coupling material that can significantly prolong the interval between re-coupling.

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 score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.004

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 head0.025
GPT teacher head0.254
Teacher spread0.229 · 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
Published2023
Admission routes1
Has abstractyes

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