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Native Barcoding (SQK-NBD114) gDNA for Adaptive Sampling using Oxford Nanopore Technologies v1

2023· preprint· en· W4390349396 on OpenAlexfundno aff
Hannah Macpherson, Emil K. Gustavsson, Jasmaine Lee, Zhongbo Chen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicAlgorithms and Data Compression
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchMedical Research CouncilMichael J. Fox Foundation for Parkinson's Research
Keywordsgenomic DNANanoporeNanopore sequencingDNA barcodingSampling (signal processing)Computational biologyDNADNA sequencerBiologyDNA sequencingEvolutionary biologyComputer scienceGeneticsNanotechnologyMaterials scienceTelecommunications

Abstract

fetched live from OpenAlex

This protocol describes how to carry out native barcoding of genomic DNA (gDNA) using the Native Barcoding Kit 24 V14 (SQK-NBD114.24) for Adaptive Sampling using Oxford Nanopore Technologies (ONT). Adaptive Sampling is a method of real-time selection of DNA molecules for sequencing and rejection of DNA molecules that are not of interest by reversing the voltage across the pores.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.024
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.025

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 head0.183
GPT teacher head0.350
Teacher spread0.167 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations2
Published2023
Admission routes1
Has abstractyes

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