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Record W2424236023 · doi:10.1002/pmic.201500490

Three‐dimensional imaging MS of lipids in atherosclerotic plaques: Open‐source methods for reconstruction and analysis

2016· article· en· W2424236023 on OpenAlexafffund
Nathan Heath Patterson, Robert J. Doonan, Stella S. Daskalopoulou, Martin Dufresne, Sébastien Lenglet, Fabrizio Montecucco, Aurélien Thomas, Pierre Chaurand

Bibliographic record

VenuePROTEOMICS · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsMcGill UniversityUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsVisualizationComputer scienceSegmentationOpen sourceSample (material)SoftwareArtificial intelligenceData scienceComputer visionPattern recognition (psychology)ChemistryChromatography

Abstract

fetched live from OpenAlex

Three-dimensional MALDI imaging MS (IMS) is a growing branch of IMS still requiring developments in methodology and technology to make the technique routinely accessible. Many challenges are simply a matter of producing 3D reconstructions and interpreting them in a timely fashion. In this aim and using analysis of lipids from atherosclerotic plaques from a human carotid and mouse aortic sinuses, we describe 3D reconstruction methods using open-source software that provides high-quality visualization and rapid interpretation through multivariate segmentation of the 3D IMS data. Multiple datasets were generated for each sample and we provide insight into simple means to correlate the separate datasets.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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
GenreMethods

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

Citations57
Published2016
Admission routes2
Has abstractyes

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