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Record W2116062363 · doi:10.1093/bioinformatics/btp011

SCARF: maximizing next-generation EST assemblies for evolutionary and population genomic analyses

2009· article· en· W2116062363 on OpenAlexaff
Michael S. Barker, Katrina M. Dlugosch, Archana Reddy, Sarah N. Amyotte, Loren H. Rieseberg

Bibliographic record

VenueBioinformatics · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContigLicenseSequence assemblySequence (biology)Computer scienceMIT LicenseSoftwareGenomicsComputational biologyPopulationBiologyProgramming languageGenomeGeneticsOperating systemGene

Abstract

fetched live from OpenAlex

Abstract Summary: Scaffolded and Corrected Assembly of Roche 454 (SCARF) is a next-generation sequence assembly tool for evolutionary genomics that is designed especially for assembling 454 EST sequences against high-quality reference sequences from related species. The program was created to knit together 454 contigs that do not assemble during traditional de novo assembly, using a reference sequence library to orient the 454 sequences. Availability: SCARF is freely available at http://msbarker.com/software.htm, and is released under the open source GPLv3 license (http://www.opensource.org/licenses/gpl-3.0.html. Contact: msbarker@indiana.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.452

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)0.0000.000

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 head0.066
GPT teacher head0.297
Teacher spread0.231 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations21
Published2009
Admission routes1
Has abstractyes

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