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Record W2167844024 · doi:10.1038/nmeth.2557

The CRAPome: a contaminant repository for affinity purification–mass spectrometry data

2013· article· en· W2167844024 on OpenAlexafffund
Dattatreya Mellacheruvu, Zachary Wright, Amber L. Couzens, Jean‐Philippe Lambert, Nicole St‐Denis, Li Tuo, Yana Miteva, Simon Hauri, Mihaela E. Sardiu, Teck Yew Low, Vincentius A. Halim, Richard D. Bagshaw, Nina C. Hubner, Abdallah Al-Hakim, Annie Bouchard, Denis Faubert, Damian Fermin, Wade H. Dunham, Marilyn Goudreault, Zhen-Yuan Lin, Beatriz Gonzalez Badillo, Tony Pawson, Daniel Durocher, Benoit Coulombe, Ruedi Aebersold, Giulio Superti‐Furga, Jacques Colinge, Albert J. R. Heck, Hyungwon Choi, Matthias Gstaiger, Shabaz Mohammed, Ileana M. Cristea, Keiryn L. Bennett, Brian Raught, Rob M. Ewing, Anne‐Claude Gingras, Alexey I. Nesvizhskii

Bibliographic record

VenueNature Methods · 2013
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsOntario Institute for Cancer ResearchUniversity of TorontoUniversité de MontréalMontreal Clinical Research InstituteLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Drug AbuseNational Cancer InstituteNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsComputational biologyMass spectrometryTandem affinity purificationChemistryProtein purificationProteomeChromatographyAffinity chromatographyBiologyBiochemistryEnzyme

Abstract

fetched live from OpenAlex
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 imitation

Not 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)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.007
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0050.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.018

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 head0.391
Teacher spread0.363 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations1,804
Published2013
Admission routes2
Has abstractno

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