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Record W4402602294 · doi:10.1038/s44185-024-00053-7

Contextualising samples: supporting reference genomes of European biodiversity through sample and associated metadata collection

2024· article· en· W4402602294 on OpenAlexafffund
Astrid Böhne, Rosa Fernández, Jennifer A. Leonard, Ann M. Mc Cartney, Seanna McTaggart, José Melo‐Ferreira, Rita Monteiro, Rebekah A. Oomen, Olga Vinnere Pettersson, Torsten H. Struck

Bibliographic record

Venuenpj Biodiversity · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversity of New Brunswick
FundersEuropean Social FundLeibniz-GemeinschaftHORIZON EUROPE European Research CouncilBiotechnology and Biological Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaHORIZON EUROPE Framework ProgrammeDirectorate for Biological SciencesScience for Life LaboratoryAgencia Estatal de InvestigaciónFundação para a Ciência e a TecnologiaLeibniz-Institut für Zoo- und WildtierforschungJames S. McDonnell FoundationNorges ForskningsrådMinisterio de Ciencia e InnovaciónRural Development AdministrationUK Research and InnovationStaatssekretariat für Bildung, Forschung und InnovationDeutsche Forschungsgemeinschaft
KeywordsWorkflowMetadataSample (material)BiodiversityData scienceResource (disambiguation)GenomeEnvironmental resource managementBiologyWorld Wide WebComputer scienceEcologyDatabase

Abstract

fetched live from OpenAlex

The European Reference Genome Atlas (ERGA) consortium aims to generate a reference genome catalogue for all of Europe's eukaryotic biodiversity. The biological material underlying this mission, the specimens and their derived samples, are provided through ERGA's pan-European network. To demonstrate the community's capability and capacity to realise ERGA's ambitious mission, the ERGA Pilot project was initiated. In support of the ERGA Pilot effort to generate reference genomes for European biodiversity, the ERGA Sampling and Sample Processing committee (SSP) was formed by volunteer experts from ERGA's member base. SSP aims to aid participating researchers through (i) establishing standards for and collecting of sample/specimen metadata; (ii) prioritisation of species for genome sequencing; and (iii) development of taxon-specific collection guidelines including logistics support. SSP serves as the entry point for sample providers to the ERGA genomic resource production infrastructure and guarantees that ERGA's high-quality standards are upheld throughout sample collection and processing. With the volume of researchers, projects, consortia, and organisations with interests in genomics resources expanding, this manuscript shares important experiences and lessons learned during the development of standardised operational procedures and sample provider support. The manuscript details our experiences in incorporating the FAIR and CARE principles, species prioritisation, and workflow development, which could be useful to individuals as well as other initiatives.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.247
Teacher spread0.189 · 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.

Study designObservational
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

Citations9
Published2024
Admission routes2
Has abstractyes

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