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Record W2107169352 · doi:10.1002/elps.200600590

Molecular deformation and free‐solution electrophoresis of DNA‐uncharged polymer conjugates at high field strengths: Theoretical predictions. Part 1: Hydrodynamic segregation

2007· article· en· W2107169352 on OpenAlexaff
Laurette C. McCormick, Gary W. Slater

Bibliographic record

VenueElectrophoresis · 2007
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Ottawa
FundersNational Human Genome Research Institute
KeywordsDragElectrophoresisDNARandom coilChemistryMacromoleculePolymerGel electrophoresisMoleculeChemical physicsNanotechnologyChromatographyMaterials scienceCrystallographyMechanicsPhysicsBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Recent advancements in DNA sequencing by end-labeled free-solution electrophoresis (ELFSE) show the promise of this novel technique which overcomes the need for a gel by using a label (or drag-tag) to render the free solution mobility of the DNA size-dependent. It is the attachment of an uncharged drag-tag molecule of a set size to various lengths of DNA in the sample that selectively slows down smaller DNA chains which have less force to pull the drag-tag than larger DNA. So far, only globally random coil conformations have been associated with ELFSE, i.e., the DNA and the label together form a single, undeformed hydrodynamic unit. This paper investigates the conditions under which the DNA and label will segregate into two hydrodynamically distinct units, based on a theoretical approach developed for the electrophoresis of polyampholytes. Optimal experimental conditions tailored to the available label sizes and voltages are predicted along with insight into ideal label architecture.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.002
GPT teacher head0.176
Teacher spread0.174 · 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 designTheoretical or conceptual
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

Citations12
Published2007
Admission routes1
Has abstractyes

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