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Record W2162093367 · doi:10.1103/physreve.64.051605

Interface pinning in spontaneous imbibition

2001· article· en· W2162093367 on OpenAlexaff
Marc A. Dubé, S. Majaniemi, M. Rost, Mikko J. Alava, K. R. Elder, Tapio Ala-Nissilä

Bibliographic record

VenuePhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsMcGill University
FundersDivision of Materials ResearchAcademy of FinlandNational Science Foundation
KeywordsDimensionless quantityImbibitionCondensed matter physicsPhysicsLength scaleMean field theoryExponentMaterials scienceThermodynamicsMechanics

Abstract

fetched live from OpenAlex

Evaporation and gravity induced pinning in spontaneous imbibition are examined within a phase field formalism. Evaporation is introduced via a nonconserving term and gravity through a convective term that constrains the influx of liquid. Their effects are described by dimensionless coupling constants $\ensuremath{\epsilon}$ and g, respectively. From liquid conservation, the early time behavior of the average interface position follows $H(t)\ensuremath{\sim}{t}^{1/2}$ until a crossover time ${t}^{*}(g,\ensuremath{\epsilon}).$ After that the pinning height ${H}_{p}(g,\ensuremath{\epsilon})$ is approached exponentially in time, in accordance with mean field theory. The statistical roughness of the interface is described by an exponent $\ensuremath{\chi}\ensuremath{\simeq}1.25$ at all stages of the rise, but the dynamic length scale controlling roughness crosses over from ${\ensuremath{\xi}}_{\ifmmode\times\else\texttimes\fi{}}\ensuremath{\sim}{H}^{1/2}$ to a time independent pinning length scale ${\ensuremath{\xi}}_{p}(\ensuremath{\epsilon},g).$

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.304
Teacher spread0.297 · 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

Citations16
Published2001
Admission routes1
Has abstractyes

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