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Record W2157252391 · doi:10.1002/rcm.3668

Software for the calculation of isotope patterns in tandem mass spectrometry

2008· article· en· W2157252391 on OpenAlexafffund
Louis Ramaley, Lisandra Cubero Herrera

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2008
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsDalhousie University
FundersNational Research Council CanadaDalhousie University
KeywordsChemistryDeconvolutionTandemIsotopeSoftwareResolution (logic)Analytical Chemistry (journal)Tandem mass spectrometryAnalyteMass spectrometryIonProduct (mathematics)AlgorithmChromatographyComputer scienceNuclear physicsArtificial intelligence

Abstract

fetched live from OpenAlex

Software, available at no cost on the Internet, is described which uses polynomial expansion algorithms to calculate the isotope patterns for precursor ion, neutral loss, and MSn product ion tandem mass spectra. Such information is useful for determining product ion and neutral loss composition, identification of analytes in complex samples, deconvolution of overlapping spectra, and correction of peak heights or areas in quantitative analysis. The effect of less than unit mass resolution on the isotope patterns is described and experimental examples of the use of the software are presented.

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)0.005
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.073
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0730.050

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 head0.034
GPT teacher head0.297
Teacher spread0.263 · 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
GenreSoftware

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

Citations52
Published2008
Admission routes2
Has abstractyes

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