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Enhancing untargeted metabolomics using metadata-based source annotation
Julia M. Gauglitz, Kiana West, Wout Bittremieux, Candace L. Williams, Kelly C. Weldon, Morgan Panitchpakdi, Francesca Di Ottavio, Christine M. Aceves, Elizabeth A. R. Brown, Nicole Sikora, Alan K. Jarmusch, Cameron Martino, Anupriya Tripathi, Michael J. Meehan, Kathleen Dorrestein, Justin P. Shaffer, Roxana Coras, Fernando Vargas, Lindsay DeRight Goldasich, Tara Schwartz, MacKenzie Bryant, Gregory Humphrey, Abigail J. Johnson, Katharina Spengler, Pedro Belda‐Ferre, Edgar Diaz, Daniel McDonald, Qiyun Zhu, Emmanuel O. Elijah, Mingxun Wang, Clarisse Marotz, Kate E. Sprecher, Daniela Vargas-Robles, Dana Withrow, Gail Ackermann, Lourdes Herrera, B.J. Bradford, Lucas Maciel Mauriz Marques, Juliano Geraldo Amaral, Rodrigo Moreira da Silva, Flávio P. Veras, Thiago M. Cunha, Renê Donizeti Ribeiro de Oliveira, Paulo Louzada‐Júnior, Robert H. Mills, Paulina K. Piotrowski, Stephanie L. Servetas, Sandra M. Da Silva, Christina M. Jones, Nancy J. Lin, Katrice A. Lippa, Scott A. Jackson, Rima Kaddurah Daouk, Douglas Galasko, Parambir S. Dulai, Tatyana Kalashnikova, Curt Wittenberg, Robert Terkeltaub, Megan M. Doty, Jae Kim, Kyung E. Rhee, Julia Beauchamp‐Walters, Kenneth P. Wright, Maria Gloria Domínguez-Bello, Mark Manary, Michelli F. Oliveira, Brigid S. Boland, Norberto Peporine Lopes, Mónica Gumá, Austin D. Swafford, Rachel J. Dutton, Rob Knight, Pieter C. Dorrestein
Bibliographic record
VenueNature Biotechnology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Minority Health and Health DisparitiesNational Institute of Child Health and Human DevelopmentNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Center for Complementary and Integrative HealthNational Institute on AgingMultidisciplinary University Research InitiativeUniversity of California, San DiegoNational Institutes of HealthIXICOMedelaNational Institute of General Medical SciencesH. Lundbeck A/SNational Institute of Food and AgricultureSwedish Orphan BiovitrumGenentechVlaamse regeringServierGeorgia Clinical and Translational Science AllianceEisaiConselho Nacional de Desenvolvimento Científico e TecnológicoCanadian Institutes of Health ResearchFonds Wetenschappelijk OnderzoekFundação de Amparo à Pesquisa do Estado de São PauloU.S. Department of Health and Human ServicesDeutscher Akademischer AustauschdienstDanone Nutricia ResearchNational Institute of Standards and TechnologyNorthern California Institute for Research and EducationDanoneUniversity of Southern CaliforniaPfizerBioClinicaBiogenOffice of Naval ResearchU.S. Department of AgricultureNational Heart, Lung, and Blood InstituteNovartis Pharmaceuticals CorporationInstitute for the Advancement of Food and Nutrition SciencesEli Lilly and CompanyBristol-Myers SquibbNational Center for Advancing Translational SciencesMeso Scale DiagnosticsAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsAnnotationMetabolomicsMetadataComputational biologyComputer scienceInformation retrievalWorld Wide WebBiologyBioinformaticsArtificial intelligence
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 distilled prediction
Teacher imitationNot 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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.947
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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 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
Citations77
Published2022
Admission routes1
Has abstractno