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Record W2146498815 · doi:10.1109/iembs.2009.5332639

Simulation of a presynaptic nerve terminal with a tethered particle system model

2009· article· en· W2146498815 on OpenAlexaff
Rhys Goldstein, Gabriel Wainer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiffusion and Search Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsTerminal (telecommunication)Nerve cellsBiological systemSynaptic vesicleImpulse (physics)Particle (ecology)BiophysicsVesicleFree nerve endingSynapsinComputer scienceChemistryMechanicsMembranePhysicsAnatomyBiologyCell biologyClassical mechanics

Abstract

fetched live from OpenAlex

Presynaptic nerve terminals are located at the ends of nerve cells; a signal propagating through a nerve cell reaches one of these compartments before being transmitted to an adjacent nerve cell. A tethered particle system (TPS) is a type of impulse-based model recently developed for the simulation of deformable biological structures. In a TPS, collisions can cause approaching particles to rebound outwards, as one would expect, but they can also caused separating particles to retract inwards. This paper demonstrates how a TPS can be used to simulate biological systems by presenting its application to a presynaptic nerve terminal. The model captures the clustering of sacs called vesicles in the presence of protein called synapsin. Both rigid and deformable membranes are also described. The simulated presynaptic nerve terminal may be used, for example, to predict how a change in synapsin concentration affects the size of vesicle clusters.

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: Methods · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.266
Teacher spread0.253 · 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
GenreMethods

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

Citations4
Published2009
Admission routes1
Has abstractyes

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