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Record W2767559908 · doi:10.1039/c7an01629h

Expanding the structural analysis capabilities on an Orbitrap-based mass spectrometer for large macromolecular complexes

2017· article· en· W2767559908 on OpenAlexaff
Kyle L. Fort, Michiel van de Waterbeemd, Dmitriy Boll, Maria Reinhardt-Szyba, Mikhail E. Belov, Eita Sasaki, Reinhard Zschoche, Donald Hilvert, Alexander Makarov, Albert J. R. Heck

Bibliographic record

VenueThe Analyst · 2017
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsThermo Fisher Scientific (Canada)
FundersH2020 Future and Emerging TechnologiesNederlandse Organisatie voor Wetenschappelijk OnderzoekStichting voor Fundamenteel Onderzoek der MaterieEuropean Commission
KeywordsOrbitrapMass spectrometryMacromoleculeChemistrySpectrometerChromatographyAnalytical Chemistry (journal)PhysicsOpticsBiochemistry

Abstract

fetched live from OpenAlex

Native mass spectrometry can provide insight into the structure of macromolecular biological systems. As analytes under investigation get larger and more complex, instrument capabilities need to be advanced. Herein, modifications to an Orbitrap Q Exactive Plus mass spectrometer that increase signal intensity, mass resolution, and maximum m/z measurable are described.

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 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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.022
GPT teacher head0.317
Teacher spread0.295 · 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 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

Citations138
Published2017
Admission routes1
Has abstractyes

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