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Record W2954772748 · doi:10.3897/biss.3.37338

Mainstreaming Molecular Biodiversity: A call for a unified and interoperable framework

2019· article· en· W2954772748 on OpenAlexaboutno aff
Pier Luigi Buttigieg, Jerry Lanfear, Frank Oliver Glöckner, James Macklin

Bibliographic record

VenueBiodiversity Information Science and Standards · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityInteroperabilityData scienceBiologyEnvironmental resource managementEcologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Over the past 20 years, immense progress has been made in enhancing the effectiveness, affordability, and deployability of molecular methods for biodiversity assessment and monitoring. From the micro- to macroscopic scale, methods such as amplicon sequencing of phylogenetic marker genes, metagenomics, and metatranscriptomics have greatly impacted biology and ecology, and are steadily being integrated into national and international biodiversity policy. Over the next decade, technologies such as miniaturised and autonomous DNA sequencing platforms will amplify this momentum, ushering in an unprecedented volume of deeply minable biodiversity information. While production-grade resources exist to standardise, archive, and exchange raw molecular data (e.g. the resources of the International Nucleotide Sequence Database Collaboration (INSDC) for DNA and RNA sequences), there are still no equivalent frameworks for biodiversity information derived from molecular methods. Research infrastructures in both the biodiversity and molecular biology domains must fill this gap with great urgency to channel molecular advances into efforts to understand and sustain Earth's imperilled biosphere. This session seeks to accelerate the implementation of global standards to link molecular biodiversity data to taxonomy-based systems. Only with these in place can we realise a robust, distributed, yet fully interoperating, network of infrastructures, projects, and researchers addressing molecular biodiversity. This introductory series of flash talks will present the rationale and goals of the session, alongside a joint vision from representatives of several convening stakeholders. A contribution from ELIXIR, an intergovernmental organisation of distributed infrastructures for biological data, will demonstrate the high readiness of biological data resources such as the European Nucleotide Archive (ENA) to mobilise molecular data along new standards. An intervention from the SILVA rRNA database project - itself an ELIXIR Core Data Resource - will note the actionability of interfacing molecular-based phylogenies with Linnaean systems hosted by partners such as the Global Biodiversity Information Facility (GBIF). Two more contributions will emphasise the essential role (and thus critical need) of molecular biodiversity standards in bridging research and operations. The first will focus on the nation-scale Metagenomics-Based Ecosystem Biomonitoring (EcoBiomics) project in Canada, which is using 'omic approaches to better assess, monitor, and remediate microbial and invertebrate biodiversity in soil and aquatic ecosystems, thus sustaining ecosystem resilience and service provision upon which society and economies depend. The second will underscore the need for international and stable standards to advance the long-term mission of the Global Omics Observatory Network (GLOMICON), and its contribution to the Global Ocean Observing System's Essential Ocean Variables (GOOS EOVs) under the Intergovernmental Oceanographic Commission of the United Nations Educational, Scientific, and Cultural Organization (IOC-UNESCO). Collectively, these contributions will make the case for a concerted effort to expedite the principled creation of operational information standards in molecular biodiversity. We invite all stakeholders to join us in implementing these standards in the coming years.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.308

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 studies0.0000.000
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 head0.008
GPT teacher head0.229
Teacher spread0.221 · 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 designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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