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Record W3194892368 · doi:10.1088/1367-2630/ac37c6

Electromechanical feedback control of nanoscale superflow

2021· preprint· en· W3194892368 on OpenAlexafffund
Emil Varga, J. P. Davis

Bibliographic record

VenueNew Journal of Physics · 2021
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsHelmholtz free energyPhysicsResonatorOscillation (cell signaling)SuperfluidityDisplacement (psychology)Helmholtz resonatorCoherence (philosophical gambling strategy)AcousticsVorticityVortexMechanicsCondensed matter physicsQuantum mechanicsOptics

Abstract

fetched live from OpenAlex

Abstract Superfluid 4He is a promising material for optomechanical and electromechanical applications due to its low acoustic loss. Some of the more intriguing aspects of superfluidity—the macroscopic coherence, topological nature of vorticity, and capability of supporting non-classical flows—remain, however, poorly explored resources in opto- and electro-mechanical systems. Here, we present an electromechanical coupling to pure superflow inside a nanofluidic Helmholtz resonator with viscously clamped normal fluid. The system is capable of simultaneous measurement of displacement and velocity of the Helmholtz mechanical mode weakly driven by incoherent environmental noise. Additionally, we implement feedback capable of inducing self-oscillation of the non-classical acoustic mode, damping the motion below the ambient level, and tuning of the mode frequency.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.012
GPT teacher head0.243
Teacher spread0.230 · 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
Published2021
Admission routes2
Has abstractyes

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