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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 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)0.001
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 score0.536
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.058
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 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

Citations109
Published2022
Admission routes1
Has abstractyes

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