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Record W4413465154 · doi:10.1039/d5sd00091b

Evaluation of machine learning and deep learning models for the classification of a single extracellular vesicles spectral library

2025· article· en· W4413465154 on OpenAlexafffund
Carolina del Real Mata, Yao Lü, Mahsa Jalali, Andrei Bocan, Maryam Khatami, Laura Montermini, Jackson McCormack-Ilersich, Walter Reisner, Livia Garzia, Janusz Rak, Danilo Bzdok, Sara Mahshid

Bibliographic record

VenueSensors & Diagnostics · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsMcGill University Health CentreMcGill University
FundersFonds de recherche du Québec – Nature et technologiesFondation Charles-BruneauCanadian Cancer Society Research InstituteCanada Research ChairsMcGill UniversityMcGill University Health CentreCanadian Cancer SocietyCanadian Institutes of Health ResearchUniversité du Québec à MontréalCMC MicrosystemsFondation Brain CanadaInstitut de recherche, Centre universitaire de santé McGill
KeywordsExtracellular vesiclesArtificial intelligenceDeep learningComputer scienceVesicleExtracellularSimple (philosophy)Machine learningChemistryBiologyCell biologyBiochemistryPhilosophyMembraneEpistemology

Abstract

fetched live from OpenAlex

Nanostructure-based sensors study extracellular vesicles; optimization of a single-vesicle resolution spectral library to enhance classification for future AI-driven diagnostics.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.269
Teacher spread0.244 · 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 designSimulation or modeling
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

Citations4
Published2025
Admission routes2
Has abstractyes

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