MétaCan
Menu
Back to cohort

Pattern formation in coiling of falling viscous threads: Revisiting the geometric model

2025· article· en· W4407156567 on OpenAlexafffund
Eusebius J. Doedel, Ida Karimfazli, Behrooz Yousefzadeh

Bibliographic record

VenuePhysical Review Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsConcordia University
FundersUniversity of AucklandNatural Sciences and Engineering Research Council of CanadaUniversity of Cambridge
KeywordsFalling (accident)MathematicsGeometryMechanicsPhysicsPsychology

Abstract

fetched live from OpenAlex

A flowing viscous thread in contact with a moving platform forms intricate patterns. The shape of these patterns depends on the fall height and the platform's speed. At moderate fall heights, these patterns can be reproduced using a model based on the no-slip condition and the curvature of the thread at the contact point. We re-examine these patterns experimentally and revisit the model computationally. We discover new patterns in both cases, highlighting the presence of greater complexity within the phenomenon than known previously.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.295
Teacher spread0.276 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

Same venuePhysical Review FluidsSame topicSoft Robotics and ApplicationsFrench-language works237,207