MétaCan
Menu
Back to cohort
Record W2330719462 · doi:10.1103/physreve.91.012705

Confinement-dependent localization of diffusing aggregates in cellular geometries

2015· article· en· W2330719462 on OpenAlexafffund
M. Rezaei, Vaihbav Wasnik, Liyan Ping, Dibyendu Das, Eldon Emberly

Bibliographic record

VenuePhysical Review E · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCluster (spacecraft)PolarMonte Carlo methodPhysicsShell (structure)Bent molecular geometryEntropy (arrow of time)Statistical physicsChemical physicsMaterials scienceComputer scienceMathematics

Abstract

fetched live from OpenAlex

Confinement has a strong influence on diffusing nano-sized clusters. In particular, biomolecular aggregates within the shell-like confining space of a bacterial cell have been shown to display a variety of localization patterns, from being midcell to the poles. How does the confining space determine where the aggregate will localize? Here, using Monte Carlo simulations we have calculated the equilibrium spatial distribution of fixed-sized clusters diffusing in spherocylindrical shells. We find that localization to the poles depends strongly on shell thickness and the size of the cluster. Compared to being at midcell, polar clusters can be more bent and hence have higher energy, but they also can have a greater number of defects and hence have more entropy. Under certain conditions this can lead to polar clusters having a lower free energy than at midcell, favoring localization to the poles. Our findings suggest possible localization selection mechanisms within shell-like geometries that can arise purely from cluster confinement.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.281
Teacher spread0.249 · 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 teacher head, 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

Citations0
Published2015
Admission routes2
Has abstractyes

Explore more

Same venuePhysical Review ESame topicMaterial Dynamics and PropertiesFrench-language works237,207