MétaCan
Menu
Back to cohort
Record W2140460797 · doi:10.1101/pdb.prot5506

Efficient Genomic DNA Extraction from Low Target Concentration Bacterial Cultures Using SCODA DNA Extraction Technology

2010· article· en· W2140460797 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCold Spring Harbor Protocols · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsCollège Boréal
Fundersnot available
KeywordsNucleic acidMetagenomicsgenomic DNADNA extractionDNANucleic acid quantitationChromatographyExtraction (chemistry)Sample preparationChemistryComputational biologyBiologyPolymerase chain reactionBiochemistryGene

Abstract

fetched live from OpenAlex

Methods for the extraction of nucleic acids are straightforward in instances where there is ample nucleic acid mass in the sample and contamination is minimal. However, applications in areas such as metagenomics, life science research, clinical research, and forensics, that are limited by smaller amounts of starting materials or more dilute samples, require sample preparation methods that are more efficient at extracting nucleic acids. Synchronous coefficient of drag alteration (SCODA) is a novel electrophoretic nucleic acid purification technology that has been tested successfully with both highly contaminated and dilute samples and is a promising candidate for new sample preparation challenges. In this article, as an example of SCODA's performance with limited sample material, we outline a genomic DNA (gDNA) extraction protocol from low abundance cultures of Escherichia coli DH10B. This method is equally well suited to high biomass samples.

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.

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 categoriesnone
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 score0.055
Threshold uncertainty score0.958

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.297
Teacher spread0.288 · 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