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Record W4393748146 · doi:10.5281/zenodo.7526395

Baseline assemblies for "ntLink: a toolkit for de novo genome assembly scaffolding and mapping using long reads" protocol

2023· dataset· en· W4393748146 on OpenAlexaff
Lauren Coombe, René L. Warren, Johnathan Wong, Vladimir Nikolić, İnanç Birol

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsCanada's Michael Smith Genome Sciences Centre
Fundersnot available
KeywordsScaffoldProtocol (science)Sequence assemblyBaseline (sea)Computational biologyComputer scienceScaffold proteinBiologyGeneticsDatabaseMedicineGene

Abstract

fetched live from OpenAlex

ntLink is a flexible de novo genome scaffolding toolkit which can be run in various modes depending on the desired user output, with multiple new functionalities recently introduced. Here, we provide the baseline assembly datasets used in the ntLink protocol paper "ntLink: a toolkit for de novo genome assembly scaffolding and mapping using long reads". The provided assemblies are ABySS (short-read) and Flye (long-read) assemblies of Caenorhabditis elegans genome sequencing data. The ABySS (v2.1.4) assembly utilized paired-end short reads (accession DRR008444), and was run with the following parameters: k=64 l=40 s=1000 q=15 B=10G j=8 kc=3 H=4 S=1000-10000 N=9.The C. elegans Flye (v2.5) assembly was run using Oxford Nanopore long reads (accession SRR10028109) and the following parameters: --nano-raw SRR10028109.fastq -g100m -t48.

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)0.004
metaresearch head score (Gemma)0.008
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.064
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0060.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0640.097

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 head0.059
GPT teacher head0.302
Teacher spread0.243 · 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
GenreDataset

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

Citations0
Published2023
Admission routes1
Has abstractyes

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