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Record W4205846574 · doi:10.1073/pnas.2115639118

Standards recommendations for the Earth BioGenome Project

2022· article· en· W4205846574 on OpenAlexaff
Mara Lawniczak, Richard Durbin, Paul Flicek, Kerstin Lindblad‐Toh, Xiaofeng Wei, John M. Archibald, William J. Baker, Katherine Belov, Mark Blaxter, Tomàs Marquès‐Bonet, Anna K. Childers, Jonathan A. Coddington, Keith A. Crandall, Andrew J. Crawford, Robert Davey, Federica Di Palma, Qi Fang, Wilfried Haerty, Neil Hall, Katharina J. Hoff, Kerstin Howe, Erich D. Jarvis, Warren E. Johnson, Rebecca N. Johnson, Paul Kersey, Xin Liu, Jose V. Lopez, Eugene W. Myers, Olga Vinnere Pettersson, Adam M. Phillippy, Monica F. Poelchau, Kim D. Pruitt, Arang Rhie, Sunil Kumar Sahu, Nicholas A. Salmon, Pamela S. Soltis, David Swarbreck, Françoise Thibaud‐Nissen, Sibo Wang, Jill Wegrzyn, Guojie Zhang, He Zhang, Harris A. Lewin, Stephen Richards

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsGenome British ColumbiaDalhousie University
FundersU.S. National Library of MedicineBiotechnology and Biological Sciences Research CouncilNational Museum of Natural HistoryEuropean Molecular Biology LaboratorySmithsonian InstitutionWellcome TrustHoward Hughes Medical InstituteVillum FondenNational Institutes of HealthVetenskapsrådetNational Science Foundation
KeywordsFlexibility (engineering)InformaticsComputer scienceEngineering managementKnowledge managementData sciencePolitical scienceEngineeringManagement

Abstract

fetched live from OpenAlex

A global international initiative, such as the Earth BioGenome Project (EBP), requires both agreement and coordination on standards to ensure that the collective effort generates rapid progress toward its goals. To this end, the EBP initiated five technical standards committees comprising volunteer members from the global genomics scientific community: Sample Collection and Processing, Sequencing and Assembly, Annotation, Analysis, and IT and Informatics. The current versions of the resulting standards documents are available on the EBP website, with the recognition that opportunities, technologies, and challenges may improve or change in the future, requiring flexibility for the EBP to meet its goals. Here, we describe some highlights from the proposed standards, and areas where additional challenges will need to be met.

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)0.054
metaresearch head score (Gemma)0.093
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: Methods · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.093
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0130.014
Science and technology studies0.0030.003
Scholarly communication0.0100.010
Open science0.0080.005
Research integrity0.0120.011
Insufficient payload (model declined to judge)0.0330.031

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 head0.058
GPT teacher head0.336
Teacher spread0.278 · 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
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

Citations109
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicGenomics and Phylogenetic StudiesFrench-language works237,207