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Record W1993989189 · doi:10.1089/omi.2006.10.289

Carbohydrate Microarrays: Survey of Fabrication Techniques

2006· review· en· W1993989189 on OpenAlexafffund
Adrian S. Culf, Miroslava Čuperlović‐Culf, Rodney J. Ouellette

Bibliographic record

VenueOMICS A Journal of Integrative Biology · 2006
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsAtlantic Cancer Research Institute
FundersAtlantic Canada Opportunities Agency
KeywordsGlycomeGlycanDECIPHERDNA microarrayComputational biologyGlycomicsGenomeBiologyComputer scienceGeneBioinformaticsBiochemistryGlycoproteinGene expression

Abstract

fetched live from OpenAlex

Carbohydrate microarrays are being developed in order to decipher the information content of the glycome. This postgenomic activity is necessary because of the complexity of protein biosynthesis and post-translational modifications that cannot currently be detected at the genome level. This review looks, in detail, at the experimental approaches that have been taken in the fabrication and preparation of carbohydrate microarrays, glycan arrays and glyco-chips. Tether structures, glycan solution preparation, detection methods and applications have been gathered together in a tabular format.

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.005

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 head0.044
GPT teacher head0.372
Teacher spread0.328 · 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

Citations59
Published2006
Admission routes2
Has abstractyes

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