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A reference map of potential determinants for the human serum metabolome
Noam Bar, Tal Korem, Omer Weissbrod, David Zeevi, Daphna Rothschild, Sigal Leviatan, Noa Kosower, Maya Lotan‐Pompan, Adina Weinberger, Caroline Le Roy, Cristina Menni, Alessia Visconti, Mario Falchi, Tim D. Spector, Henrik Vestergaard, Manimozhiyan Arumugam, Torben Hansen, Kristine H. Allin, Tue H. Hansen, Mun‐Gwan Hong, Jochen M. Schwenk, Ragna S. Häussler, Matilda Dale, Toni Giorgino, Marianne Rodriquez, Mandy H. Perry, Rachel Nice, Timothy J. McDonald, Andrew T. Hattersley, Angus G. Jones, Ulrike Graefe‐Mody, Patrick Baum, Rolf Grempler, Cecilia Engel Thomas, Federico De Masi, Caroline Brorsson, Gianluca Mazzoni, Rosa Lundbye Allesøe, Simon Rasmussen, Valborg Guðmundsdóttir, Agnes Martine Nielsen, Karina Banasik, Konstantinos D. Tsirigos, Birgitte Nilsson, Helle Pedersen, Søren Brunak, Tugce Karaderi, Agnete Troen Lundgaard, Joachim Johansen, Ramneek Gupta, Peter Wad Sackett, J. Tillner, Thorsten Lehr, Nina Scherer, Christiane Dings, Iryna Sihinevich, Heather Loftus, Louise Cabrelli, Donna McEvoy, Andrea Mari, Roberto Bizzotto, Andrea Tura, Leen M. ‘t Hart, Koen F. Dekkers, Nienke van Leeuwen, Roderick C. Slieker, Femke Rutters, Joline W. J. Beulens, Giel Nijpels, Anitra D.M. Koopman, Sabine van Oort, Lenka Groeneveld, Leif Groop, Petra J. M. Elders, Ana Viñuela, Anna Ramisch, Emmanouil Dermitzakis, Beate Ehrhardt, Christopher Jennison, Philippe Froguel, Mickaël Canouil, Amélie Boneford, Ian McVittie, Dianne Wake, Francesca Frau, Hans‐Henrik Stærfeldt, Kofi P. Adragni, Melissa K. Thomas, Han Wu, Imre Pavo, Birgit Steckel-Hamann, Henrik S. Thomsen, Giuseppe N. Giordano, Hugo Fitipaldi, Martin Ridderstråle, Azra Kurbasic, Naeimeh Atabaki Pasdar, Hugo Pomares‐Millan, Pascal M. Mutie, Robert W. Koivula, Nicky McRobert, Mark I. McCarthy, Agata Wesolowska‐Andersen, Anubha Mahajan, Moustafa Abdalla, Juan Fernandez, Reinhard W. Holl, Alison Heggie, Harshal Deshmukh, Anita M. Hennige, Susanna Bianzano, Barbara Thorand, Sapna Sharma, Harald Grallert, Jonathan Adam, Martina Troll, Andreas Fritsche, Anita Hill, Claire E. Thorne, Michelle Hudson, Teemu Kuulasmaa, Jagadish Vangipurapu, Markku Laakso, Henna Cederberg, Tarja Kokkola, Yunlong Jiao, Stephen Gough, Neil Robertson, Hélène Verkindt, Violeta Raverdi, Robert Caïazzo, François Pattou, Margaret H. White, Louise A. Donnelly, Andrew Brown, David Davtian, Adem Y. Dawed, Ian Forgie, Ewan R. Pearson, Hartmut Ruetten, Petra Musholt, Jimmy D. Bell, E. Louise Thomas, Brandon Whitcher, Mark Haid, Claudia Nicolay, Miranda Mourby, Jane Kaye, Nisha Shah, Harriet Teare, Gary Frost, Bernd Jablonka, Mathias Uhlén, Rebeca Eriksen, Josef Korbinian Vogt, Avirup Dutta, Anna Jönsson, Line Engelbrechtsen, Annemette Forman, Nadja B. Søndertoft, Nathalie de Préville, Tania Baltauss, Mark Walker, Johann Gassenhuber, Maria Klintenberg, Margit Bergstrom, Jorge Ferrer, Jerzy Adamski, Paul W. Franks, Oluf Pedersen, Eran Segal
Bibliographic record
VenueNature · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsCanadian Institute for Advanced Research
FundersEuropean CommissionNational Institute for Health and Care ResearchMedical Research CouncilCanadian Institute for Advanced ResearchEuropean Federation of Pharmaceutical Industries and AssociationsDivision of ChemistryCouncil for Higher Education
KeywordsMetabolomeMicrobiomeMetaboliteMetabolomicsHuman microbiomeBiologyComputational biologyAttributionGut microbiomeBioinformaticsGeneticsPhysiologyBiochemistryPsychology
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 head0.286
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
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
Citations449
Published2020
Admission routes2
Has abstractno