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Record W2581785370 · doi:10.1063/1.4974099

Velocity measurement by coherent x-ray heterodyning

2017· article· en· W2581785370 on OpenAlexafffund
Julien Lhermitte, Michael C. Rogers, Sabine Manet, Mark Sutton

Bibliographic record

VenueReview of Scientific Instruments · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of OttawaMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsOpticsScatteringHagen–Poiseuille equationPhysicsHeterodyne (poetry)Heterodyne detectionFlow (mathematics)Coherent backscatteringForward scatterFlow measurementMixing (physics)Direct-conversion receiverComputational physicsDetectorMechanicsAcousticsLaser

Abstract

fetched live from OpenAlex

We present a small-angle coherent x-ray scattering technique used for measuring flow velocities in slow moving materials. The technique is an extension of X-ray Photon Correlation Spectroscopy (XPCS): It involves mixing the scattering from moving tracer particles with a static reference that heterodynes the signal. This acts to elongate temporal effects caused by flow in homodyne measurements, allowing for a more robust measurement of flow properties. Using coherent x-ray heterodyning, velocities ranging from 0.1 to 10 μm/s were measured for a viscous fluid pushed through a rectangular channel. We describe experimental protocols and theory for making these Poiseuille flow profile measurements and also develop the relevant theory for using heterodyne XPCS to measure velocities in uniform and Couette flows.

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)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.025
GPT teacher head0.244
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

Citations28
Published2017
Admission routes2
Has abstractyes

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