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Record W4413052290 · doi:10.1039/d5lc00514k

High-speed cell partitioning through reactive machine learning-guided inkjet printing

2025· article· en· W4413052290 on OpenAlexafffund
Eric Cheng, Glenn Chang, Haley MacDonald, Miguel Ramirez, Pamela A. Hoodless, Robin Coope, Adi Steif, Kerry Cheung

Bibliographic record

VenueLab on a Chip · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsCanada's Michael Smith Genome Sciences CentreSpinal Cord Injury BCUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaBC Cancer FoundationGenome British ColumbiaMichael Smith Health Research BCCanada Foundation for Innovation
KeywordsComputer scienceWorkflowClassifier (UML)Artificial intelligenceComputational biologyBiologyDatabase

Abstract

fetched live from OpenAlex

fluorescent imaging of cell lines confirmed a classification accuracy of 98.7%, at a rate of 0.52 seconds per single cell, under tuned spotting parameters. Genomic analysis showed low background contamination and high coverage uniformity across the genome, enabling detection of chromosomal copy number alterations. With data tracing capabilities and a convenient user interface, we expect the Isolatrix to enable large-scale profiling of a range of genomic data modalities.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.639

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 head0.016
GPT teacher head0.250
Teacher spread0.233 · 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 teacher head, 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

Citations2
Published2025
Admission routes2
Has abstractyes

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