MétaCan
Menu
Back to cohort
Record W2431641286 · doi:10.1098/rsif.2016.0175

Intermittent turbulence in flowing bacterial suspensions

2016· article· en· W2431641286 on OpenAlexfundno aff
Eleonora Secchi, Roberto Rusconi, Stefano Buzzaccaro, M. Mehdi Salek, Steven Smriga, Roberto Piazza, Roman Stocker

Bibliographic record

VenueJournal of The Royal Society Interface · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIntermittencyTurbulenceReynolds numberSuspension (topology)MechanicsPhysicsVortexLaminar flowClassical mechanicsMathematics

Abstract

fetched live from OpenAlex

Dense suspensions of motile bacteria, possibly including the human gut microbiome, exhibit collective dynamics akin to those observed in classic, high Reynolds number turbulence with important implications for chemical and biological transport, yet this analogy has remained primarily qualitative. Here, we present experiments in which a dense suspension of Bacillus subtilis bacteria was flowed through microchannels and the velocity statistics of the flowing suspension were quantified using a recently developed velocimetry technique coupled with vortex identification methods. Observations revealed a robust intermittency phenomenon, whereby the average velocity profile of the suspension fluctuated between a plug-like flow and a parabolic flow profile. This intermittency is a hallmark of the onset of classic turbulence and Lagrangian tracking revealed that it here originates from the presence of transient vortices in the active, collective motion of the bacteria locally reinforcing the externally imposed flow. These results link together two entirely different manifestations of turbulence and show the potential of the microfluidic approach to mimic the environment characteristic of certain niches of the human microbiome.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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 designObservational
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

Citations24
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of The Royal Society InterfaceSame topicMicro and Nano RoboticsFrench-language works237,207