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Record W2528869593 · doi:10.11159/ffhmt16.160

Modelling Biological Polymeric Nanostructures in Physiological Fluids: Focus on Ribonucleic Acid Nanotubes

2016· article· en· W2528869593 on OpenAlexafffundvenue
Shyam Badu, Roderick Melnik, Sanjay Prabhakar

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of CanadaMinisterio de Economía y CompetitividadBasque Center for Applied MathematicsEusko Jaurlaritza
KeywordsNanotechnologyNanostructureFocus (optics)Materials sciencePhysics

Abstract

fetched live from OpenAlex

In this paper, we study bilogical polymeric molecular nanostructures, focusing on ribonucleic acids (RNA) and their behaviour in fluids, in particular in physiological solutions.First, we give a brief overview of the most recent results in this field related to the properties of these structures that can be characterised by root mean square deviation, radius of gyration and radial distribution function.Among other things, we provide insight into typical distributions of 23 Na + and 35 Cl -ions around the RNA nanotubes as a function of time within a distance of 5 Å from their surface.Finally, we provide details of the recent achievements in the coarse grain modeling of the such bilogical nanotubes.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.032
GPT teacher head0.243
Teacher spread0.211 · 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

Citations0
Published2016
Admission routes3
Has abstractyes

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Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicCarbon Nanotubes in CompositesFrench-language works237,207