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Record W1998047188 · doi:10.1103/physreve.80.011903

Viscous drag effect in the flexural rigidity and cantilever stiffness of bio- and nano-filaments measured with the shooting-bead method

2009· article· en· W1998047188 on OpenAlexafffund
Abdorreza Samarbakhsh, Jack A. Tuszyński

Bibliographic record

VenuePhysical Review E · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAllard Foundation
KeywordsFlexural rigidityProtein filamentCantileverDragMaterials scienceRigidity (electromagnetism)StiffnessFlexural strengthMechanicsRADIUSComposite materialPhysics

Abstract

fetched live from OpenAlex

The so-called shooting-bead method is a fast and easy experimental technique for evaluating cantilever stiffness and flexural rigidity of semiflexible to semirigid rodlike biological and nano-filaments based on the measurement of just two distances. In this paper we have derived the shooting-bead formula for cantilever stiffness and flexural rigidity taking into account the effects of the viscous drag force exerted on the filament itself. To this end, we have defined a key variable, called the filament energy-loss factor (or filament drag factor), which accounts for all the energy-loss effects. It has been shown that due to the logarithmic dependence of the filament energy-loss factor on the radius and the length of the filament, inclusion of this factor in the formula for the flexural rigidity has a very noticeable effect on the result even for very thin or long filaments. It has also been shown that the effect due to the consideration of filament energy-loss factor on calculation of the flexural rigidity increases with increasing the flexibility of the filament. We have also considered various sources of experimental error and estimated their effects.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.259

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 head0.012
GPT teacher head0.337
Teacher spread0.325 · 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 designOther design
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

Citations3
Published2009
Admission routes2
Has abstractyes

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