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Record W2048647670 · doi:10.4056/sigs.3166513

Meeting Report: Hackathon-Workshop on Darwin Core and MIxS Standards Alignment (February 2012)

2012· article· en· W2048647670 on OpenAlexfundno aff
Éamonn Ó Tuama, John Deck, Gabriel Dröge, Markus Döring, Dawn Field, Renzo Kottmann, Juncai Ma, Hiroshi Mori, Norman Morrison, Peter Sterk, Hideaki Sugawara, John Wieczorek, Linhuan Wu, Pelin Yilmaz

Bibliographic record

VenueStandards in Genomic Sciences · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsnot available
FundersNatural Environment Research CouncilInstitute of GeneticsInstitute of Microbiology, Chinese Academy of SciencesUniversity of OxfordChinese Academy of SciencesNERC Environmental Bioinformatics CentreNational Science Foundation
KeywordsPublishingControlled vocabularyLibrary scienceVocabularyDarwin (ADL)Core (optical fiber)GenomicsComputer scienceBiologyPolitical scienceWorld Wide WebGeneticsGenomeGene

Abstract

fetched live from OpenAlex

The Global Biodiversity Information Facility and the Genomic Standards Consortium convened a joint workshop at the University of Oxford, 27-29 February 2012, with a small group of experts from Europe, USA, China and Japan, to continue the alignment of the Darwin Core with the MIxS and related genomics standards. Several reference mappings were produced as well as test expressions of MIxS in RDF. The use and management of controlled vocabulary terms was considered in relation to both GBIF and the GSC, and tools for working with terms were reviewed. Extensions for publishing genomic biodiversity data to the GBIF network via a Darwin Core Archive were prototyped and work begun on preparing translations of the Darwin Core to Japanese and Chinese. Five genomic repositories were identified for engagement to begin the process of testing the publishing of genomic data to the GBIF network commencing with the SILVA rRNA database.

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)0.004
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 score0.609
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.028
GPT teacher head0.320
Teacher spread0.292 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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