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Record W1995123496 · doi:10.1103/physreve.62.699

Coherent x-ray scattering and dynamics of fluctuations in smectic-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>A</mml:mi></mml:math>and crystal-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>B</mml:mi></mml:math>films: Continuous model

2000· article· en· W1995123496 on OpenAlexaff
A. N. Shalaginov, D. E. Sullivan

Bibliographic record

VenuePhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsScatteringIntensity (physics)PhysicsDisplacement (psychology)Crystal (programming language)Dynamic light scatteringPhase (matter)Correlation function (quantum field theory)X-rayCondensed matter physicsMaterials scienceOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

We present a theoretical study of the dynamic displacement-displacement and intensity-intensity (for coherent soft x-ray scattering) correlations in Sm-A as well as Cr-B free-standing films. The work is based on a continuous hydrodynamic model that allows one to calculate efficiently the dynamic correlation functions and considerably simplifies earlier analyses of finite-size and surface effects in Sm-A films. The model is extended to Cr-B films. We show that despite the crystalline order, the Cr-B film is a strongly fluctuating system, which is due to an abnormally small shear elastic constant. An easy-shear approximation is developed to describe the fluctuations in the Cr-B phase. We predict nonmonotonic behavior of the intensity-intensity correlation function in both Sm-A and Cr-B films. The analysis can be applied to either coherent x-ray or conventional laser dynamic light scattering experiments.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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 designSimulation or modeling
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

Citations32
Published2000
Admission routes1
Has abstractyes

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Same venuePhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topicsSame topicTheoretical and Computational PhysicsFrench-language works237,207