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Record W2187335953 · doi:10.21769/bioprotoc.934

Surface Polysaccharide Extraction and Quantification

2013· article· en· W2187335953 on OpenAlexaff
Cedric A. Brimacombe, John Beatty

Bibliographic record

VenueBIO-PROTOCOL · 2013
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMembranePolysaccharideLysisBacterial outer membraneLipid bilayerBilayerPhospholipidChemistryCell envelopeLipopolysaccharideCytoplasmCell membraneBacterial capsuleBiophysicsBiochemistryBiologyEscherichia coliGene

Abstract

fetched live from OpenAlex

Gram-negative bacterial cells possess two membranes - the inner cytoplasmic membrane and the outer membrane. The two membranes are distinct in their composition; the inner membrane is composed of a phospholipid bilayer, whereas the outer membrane (OM) is composed of an asymmetrical bilayer, with the outer leaflet containing lipopolysaccharide (LPS) (Raetz and Whitfield, 2002). Surface polysaccharides, such as LPS O-antigen, or capsular polysaccharide, are often tightly associated with the OM (Whitfield, 2006). This tight association can be used to generate a rough quantification of surface polysaccharides of Gram-negative bacterial cells, as the OM can easily be dissociated from cells without associated cell lysis (Brimacombe et al., 2013). The following method describes how to quickly extract and quantify OM-associated polysaccharides.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.341
Threshold uncertainty score1.000

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)0.0010.001

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 head0.045
GPT teacher head0.375
Teacher spread0.330 · 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 designObservational
Domainnot available
GenreProtocol

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

Citations17
Published2013
Admission routes1
Has abstractyes

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