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High-Resolution, Single-Molecule Measurements of Biomolecular Motion

2007· review· en· W2114229182 on OpenAlexaff
William J. Greenleaf, Michael T. Woodside, Steven M. Block

Bibliographic record

VenueAnnual Review of Biophysics and Biomolecular Structure · 2007
Typereview
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersNational Institute of General Medical Sciences
KeywordsMagnetic tweezersOptical tweezersFörster resonance energy transferNanotechnologyResolution (logic)Tracking (education)PhysicsComputer scienceBiological systemMaterials scienceOpticsFluorescenceArtificial intelligenceBiology

Abstract

fetched live from OpenAlex

Many biologically important macromolecules undergo motions that are essential to their function. Biophysical techniques can now resolve the motions of single molecules down to the nanometer scale or even below, providing new insights into the mechanisms that drive molecular movements. This review outlines the principal approaches that have been used for high-resolution measurements of single-molecule motion, including centroid tracking, fluorescence resonance energy transfer, magnetic tweezers, atomic force microscopy, and optical traps. For each technique, the principles of operation are outlined, the capabilities and typical applications are examined, and various practical issues for implementation are considered. Extensions to these methods are also discussed, with an eye toward future application to outstanding biological problems.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.006

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 head0.021
GPT teacher head0.323
Teacher spread0.302 · 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
GenreReview

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

Citations461
Published2007
Admission routes1
Has abstractyes

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