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Record W2502755807 · doi:10.1002/adfm.201670173

Nanopore Sequencing: Graphene Nanopores for Protein Sequencing (Adv. Funct. Mater. 27/2016)

2016· article· en· W2502755807 on OpenAlexaff
James Wilson, Leila Sloman, Zhiren He, Aleksei Aksimentiev

Bibliographic record

VenueAdvanced Functional Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsNanoporeMaterials scienceGrapheneNanopore sequencingNanotechnologyProtein sequencingIonic bondingMembranePeptide sequenceBiophysicsDNA sequencingIonDNAChemistryBiochemistryBiologyGene

Abstract

fetched live from OpenAlex

In nanopore sequencing of proteins, the amino acid sequence is determined by measuring the ionic current through a nanopore in a thin membrane as the protein chain permeates through the nanopore. The computational study by A. Aksimentiev and co-workers, presented on page 4830, explores the feasibility of using nanopores in graphene for protein sequencing. The image illustrates the conformations of an unfolded protein threaded through a graphene nanopore.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.213
Teacher spread0.192 · 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.

Study designBench or experimental
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

Citations4
Published2016
Admission routes1
Has abstractyes

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