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Record W2321144071 · doi:10.1142/9781848162648_0006

MSDASH: MASS SPECTROMETRY DATABASE AND SEARCH

2008· article· en· W2321144071 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputational Systems Bioinformatics · 2008
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceMass spectrometryIon-mobility spectrometry–mass spectrometryDatabaseInformation retrievalChemistrySelected reaction monitoringChromatographyTandem mass spectrometry

Abstract

fetched live from OpenAlex

Along with the wide application of mass spectrometry in proteomics, more and more mass spectrometry data are becoming publicly available. Several public mass spectrometry data repositories have been built on the Internet. However, most of these repositories are devoid of effective searching methods. In this paper we describe a new mass spectrometry data library, and a novel method to efficiently index and search in the library for spectra that are similar to a query spectrum. A public online server have been set up and demonstrated outstanding speed and scalability of our methods. Together with the mass spectrometry library, our searching method can improve the protein identification confidence by comparing a spectrum with the ones that are already characterized in the database. The searching method can also be used alone to cluster the similar spectra in a mass spectrometry dataset together, in order to to improve the speed and accuracy of the protein identification or quantification.

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.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.027
GPT teacher head0.264
Teacher spread0.238 · 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