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Record W2116674899 · doi:10.4056/sigs.3406845

Meeting Report: 1st International Functional Metagenomics Workshop May 7–8, 2012, St. Jacobs, Ontario, Canada.

2013· article· en· W2116674899 on OpenAlexafffundabout
Katja Engel, Deborah Ashby, Sean F. Brady, Don A. Cowan, John Doemer, Elizabeth A. Edwards, Klaus M. Fiebig, Eric C. Martens, Dennis McCormac, David A. Mead, Kentaro Miyazaki, Gabriel Moreno‐Hagelsieb, Fergal O’Gara, Alexandra Reid, David R. Rose, Pascal Simonet, Sara Sjöling, Kornelia Smalla, Wolfgang R. Streit, Jennifer Tedman-Jones, Svein Valla, Elizabeth M. H. Wellington, Cheng-Cang Wu, Mark R. Liles, Josh D. Neufeld, Angela Sessitsch, Trevor C. Charles

Bibliographic record

VenueStandards in Genomic Sciences · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaWilfrid Laurier UniversityMitacsUniversity of WaterlooOntario GenomicsHealth CanadaUniversity of Toronto
FundersOntario Genomics InstituteNational Institute of General Medical SciencesNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooOntario Genomics
KeywordsMetagenomicsLibrary scienceResearch councilPolitical scienceEngineering ethicsBiologyEngineeringComputer science

Abstract

fetched live from OpenAlex

This report summarizes the events of the 1(st) International Functional Metagenomics Workshop. The workshop was held on May 7 and 8, 2012, in St. Jacobs, Ontario, Canada and was focused on building an international functional metagenomics community, exploring strategic research areas, and identifying opportunities for future collaboration and funding. The workshop was initiated by researchers at the University of Waterloo with support from the Ontario Genomics Institute (OGI), Natural Sciences and Engineering Research Council of Canada (NSERC) and the University of Waterloo.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.262
Teacher spread0.246 · 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 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

Citations3
Published2013
Admission routes3
Has abstractyes

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