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Record W4248768694 · doi:10.1002/cpps.14

Introduction to Atomic Force Microscopy (AFM) in Biology

2016· article· en· W4248768694 on OpenAlexafffund
Laurent Kreplak

Bibliographic record

VenueCurrent Protocols in Protein Science · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAtomic force microscopyNanotechnologyResolution (logic)NanometreNanoscopic scaleMicroscopyAngstromBiological materialsMaterials scienceChemistryBiological systemPhysicsComputer scienceOpticsCrystallographyBiology

Abstract

fetched live from OpenAlex

Abstract The atomic force microscope (AFM) has the unique capability of imaging biological samples with molecular resolution in buffer solution over a wide range of time scales from milliseconds to hours. In addition to providing topographical images of surfaces with nanometer‐ to angstrom‐scale resolution, forces between single molecules and mechanical properties of biological samples can be investigated from the nano‐scale to the micro‐scale. Importantly, the measurements are made in buffer solutions, allowing biological samples to “stay alive” within a physiological‐like environment while temporal changes in structure are measured—e.g., before and after addition of chemical reagents. These qualities distinguish AFM from conventional imaging techniques of comparable resolution, e.g., electron microscopy (EM). This unit provides an introduction to AFM on biological systems and describes specific examples of AFM on proteins, cells, and tissues. The physical principles of the technique and methodological aspects of its practical use and applications are also described. © 2016 by John Wiley & Sons, Inc.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0150.014

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 head0.019
GPT teacher head0.401
Teacher spread0.382 · 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 designNot applicable
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

Citations11
Published2016
Admission routes2
Has abstractyes

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